System and methods for processing analyte sensor data

ABSTRACT

Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods foranalyte sensor data processing. Particularly, the present inventionrelates to retrospectively and/or prospectively initiating acalibration, converting sensor data, updating the calibration,evaluating received reference and sensor data, and evaluating thecalibration for the analyte sensor.

BACKGROUND OF THE INVENTION

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which may cause anarray of physiological derangements (e.g., kidney failure, skin ulcers,or bleeding into the vitreous of the eye) associated with thedeterioration of small blood vessels. A hypoglycemic reaction (low bloodsugar) may be induced by an inadvertent overdose of insulin, or after anormal dose of insulin or glucose-lowering agent accompanied byextraordinary exercise or insufficient food intake.

Conventionally, a diabetic person carries a self-monitoring bloodglucose (SMBG) monitor, which typically comprises uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a diabeticwill normally only measure his or her glucose level two to four timesper day. Unfortunately, these time intervals are so far spread apartthat the diabetic will likely find out too late, sometimes incurringdangerous side effects, of a hyper- or hypo-glycemic condition. In fact,it is not only unlikely that a diabetic will take a timely SMBG value,but the diabetic will not know if their blood glucose value is going up(higher) or down (lower) based on conventional methods, inhibiting theirability to make educated insulin therapy decisions.

SUMMARY OF THE INVENTION

Systems and methods are needed that accurately provide estimated glucosemeasurements to a diabetic patient continuously and/or in real time sothat they may proactively care for their condition to safely avoidhyper- and hypo-glycemic conditions. Real time and retrospectiveestimated glucose measurements require reliable data processing in orderto provide accurate and useful output to a patient and/or doctor.

Similarly, systems and methods are needed that accurately providesubstantially continuous estimated analyte measurements for a variety ofknown analytes (e.g., oxygen, salts, protein, and vitamins) to provideprospective and/or retrospective data analysis and output to a user.

Accordingly, systems and methods are provided for retrospectively and/orprospectively calibrating a sensor, initializing a sensor, convertingsensor data into calibrated data, updating and maintaining a calibrationover time, evaluating received reference and sensor data for clinicalacceptability, and evaluating the calibration statistical acceptability,to ensure accurate and safe data output to a patient and/or doctor.

In a first embodiment a method is provided for initializing asubstantially continuous analyte sensor, the method including: receivinga data stream from an analyte sensor, including one or more sensor datapoints; receiving reference data from a reference analyte monitor,including two or more reference data points; providing at least twomatched data pairs by matching reference analyte data to substantiallytime corresponding sensor data; forming a calibration set including theat least two matching data pairs; and determining a stability of thecontinuous analyte sensor.

In an aspect of the first embodiment, the step of determining thestability of the substantially continuous analyte sensor includeswaiting a predetermined time period between about one minute and aboutsix weeks.

In an aspect of the first embodiment, the step of determining thestability of the substantially continuous analyte sensor includesevaluating at least two matched data pairs.

In an aspect of the first embodiment, the step of determining thestability of the substantially continuous analyte sensor includesevaluating one of pH, oxygen, hypochlorite, interfering species,correlation of matched pairs, R-value, baseline drift, baseline offset,and amplitude.

In an aspect of the first embodiment, the method further includesproviding one of an audible, visual, or tactile output to a user basedon the stability of the sensor.

In an aspect of the first embodiment, the step of providing output basedon the stability of the sensor includes indicating at least one of anumeric estimated analyte value, a directional trend of analyteconcentration, and a graphical representation of an estimated analytevalue.

In an aspect of the first embodiment, the step of receiving sensor dataincludes receiving sensor data from a substantially continuous glucosesensor.

In an aspect of the first embodiment, the step of receiving sensor dataincludes receiving sensor data from an implantable glucose sensor.

In an aspect of the first embodiment, the step of receiving sensor dataincludes receiving sensor data from subcutaneously implantable glucosesensor.

In an aspect of the first embodiment, the step of receiving referencedata includes receiving reference data from a self-monitoring bloodglucose test.

In an aspect of the first embodiment, the step of receiving referencedata includes downloading reference data via a cabled connection.

In an aspect of the first embodiment, the step of receiving referencedata includes downloading reference data via a wireless connection.

In an aspect of the first embodiment, the step of receiving referencedata from a reference analyte monitor includes receiving within areceiver internal communication from a reference analyte monitorintegral with the receiver.

In an aspect of the first embodiment, the step of forming a calibrationset includes evaluating at least one matched data pair using inclusioncriteria.

In an aspect of the first embodiment, the step of receiving sensor dataincludes receiving sensor data that has been algorithmically smoothed.

In an aspect of the first embodiment, the step of receiving sensor dataincludes algorithmically smoothing the received sensor data.

In an aspect of the first embodiment, the step of forming a calibrationset includes including in the calibration set between one and sixmatched data pairs.

In an aspect of the first embodiment, the step of forming a calibrationset includes including six matched data pairs.

In an aspect of the first embodiment, the step of forming a calibrationset further includes determining a value for n, where n is greater thanone and represents the number of matched data pairs in the calibrationset.

In an aspect of the first embodiment, the step of determining a valuefor n is determined as a function of the frequency of the receivedreference data points and signal strength over time.

In a second embodiment, a system is provided for initializing acontinuous analyte sensor, including: a sensor data module operativelyconnected to a continuous analyte sensor that receives a data streamincluding a plurality of time spaced sensor data points from the analytesensor; a reference input module adapted to obtain reference data from areference analyte monitor, including one or more reference data points;a processor module that forms one or more matched data pairs by matchingreference data to substantially time corresponding sensor data andsubsequently forms a calibration set including the one or more matcheddata pairs; and a start-up module associated with the processor moduleprogrammed to determine the stability of the continuous analyte sensor.

In an aspect of the second embodiment, the sensor data module is adaptedto wirelessly receive sensor data points from the sensor.

In an aspect of the second embodiment, the start-up module is programmedto wait a predetermined time period between six hours and six weeks.

In an aspect of the second embodiment, the start-up module is programmedto evaluate at least two matched data pairs.

In an aspect of the second embodiment, the start-up module is programmedto evaluate one of pH, oxygen, hypochlorite, interfering species,correlation of matched pairs, R-value, baseline drift, baseline offset,and amplitude.

In an aspect of the second embodiment, the system further includes anoutput control module associated with the processor module andprogrammed to control output of sensor data.

In an aspect of the second embodiment, the output control moduleindicates at least one of a numeric estimated analyte value, adirectional trend of analyte concentration, and a graphicalrepresentation of an estimated analyte value.

In an aspect of the second embodiment, the sensor data module isconfigured to receive sensor data from substantially the continuousglucose sensor.

In an aspect of the second embodiment, the sensor data module isconfigured to receive sensor data from an implantable glucose sensor.

In an aspect of the second embodiment, the sensor data module isconfigured to receive sensor data from subcutaneously implantableglucose sensor.

In an aspect of the second embodiment, the reference input module isconfigured to receive reference data from a self-monitoring bloodglucose test.

In an aspect of the second embodiment, the reference input module isconfigured to download reference data via a cabled connection.

In an aspect of the second embodiment, the reference input module isconfigured to download reference data via a wireless connection.

In an aspect of the second embodiment, the system further includes areference analyte monitor integral with the system and wherein thereference input module is configured to receive an internalcommunication from the reference analyte monitor.

In an aspect of the second embodiment, the processor module includesprogramming to evaluate at least one matched data pair using inclusioncriteria.

In an aspect of the second embodiment, the reference input module isconfigured to receive sensor data that has been algorithmicallysmoothed.

In an aspect of the second embodiment, the reference input module isconfigured to algorithmically smooth the received sensor data.

In an aspect of the second embodiment, the calibration set includesbetween one and six matched data pairs.

In an aspect of the second embodiment, the calibration set includes sixmatched data pairs.

In an aspect of the second embodiment, the calibration set includes nmatched data pairs, where n is greater than one.

In an aspect of the second embodiment, n is a function of the frequencyof the received reference data points and signal strength over time.

In a third embodiment, a computer system is provided for initializing acontinuous analyte sensor, the computer system including: a sensor datareceiving module that receives sensor data from the substantiallycontinuous analyte sensor via a receiver, including one or more sensordata points; a reference data receiving module that receives referencedata from a reference analyte monitor, including one or more referencedata points; a data matching module that forms one or more matched datapairs by matching reference data to substantially time correspondingsensor data; a calibration set module that forms a calibration setincluding at least one matched data pair; and a stability determinationmodule that determines the stability of the continuous analyte sensor.

In an aspect of the third embodiment, the stability determination moduleincludes a system for waiting a predetermined time period.

In an aspect of the third embodiment, the stability determination moduleevaluates at least two matched data pairs.

In an aspect of the third embodiment, the stability determination moduleevaluates one of pH, oxygen, hypochlorite, interfering species,correlation of matched pairs, R-value, baseline drift, baseline offset,and amplitude.

In an aspect of the third embodiment, the computer system furtherincludes an interface control module that provides output to the userbased on the stability of the sensor.

In an aspect of the third embodiment, the output from the interfacecontrol module includes at least one of a numeric estimated analytevalue, an indication of directional trend of analyte concentration, anda graphical representation of an estimated analyte value.

In an aspect of the third embodiment, the reference data receivingmodule is adapted to receive sensor data from a substantially continuousglucose sensor.

In an aspect of the third embodiment, the reference data receivingmodule is adapted to receive sensor data from an implantable glucosesensor.

In an aspect of the third embodiment, the reference data receivingmodule is adapted to receive sensor data from a subcutaneouslyimplantable glucose sensor.

In an aspect of the third embodiment, the reference data receivingmodule is adapted to receive sensor data from a self-monitoring bloodglucose test.

In an aspect of the third embodiment, the reference data receivingmodule is adapted to receive sensor data from a cabled connection.

In an aspect of the third embodiment, the reference data receivingmodule is adapted to download reference data via a wireless connection.

In an aspect of the third embodiment, the reference data receivingmodule is adapted to receive reference data from an internal referenceanalyte monitor that is housed integrally the computer system.

In an aspect of the third embodiment, the calibration set moduleevaluates at least one matched data pair using inclusion criteria.

In an aspect of the third embodiment, the sensor data receiving moduleis adapted to receive sensor data that has been algorithmicallysmoothed.

In an aspect of the third embodiment, the computer system furtherincludes a data smoothing module that smoothes the received sensor data.

In an aspect of the third embodiment, the calibration set moduleincludes between one and six matched data pairs.

In an aspect of the third embodiment, the calibration set moduleincludes six matched data pairs.

In an aspect of the third embodiment, the calibration set includes nnumber of matched data pairs, where n is greater than one.

In an aspect of the third embodiment, n is a function of the frequencyof the received reference data points and signal strength over time.

In a fourth embodiment, method is provided for initializing asubstantially continuous analyte sensor, the method including: receivingsensor data from a substantially continuous analyte sensor, includingone or more sensor data points; receiving reference data from areference analyte monitor, including one or more reference data points;forming one or more matched data pairs by matching reference data tosubstantially time corresponding sensor data; forming a calibration setincluding at least one matched data pair; determining stability ofcontinuous analyte sensor; and outputting information reflective of thesensor data once a predetermined level of stability has been determined.

In a fifth embodiment, a system is provided for initializing acontinuous analyte sensor, including: a sensor data module operativelylinked to a continuous analyte sensor and configured to receive one ormore sensor data points from the sensor; a reference input moduleadapted to obtain one or more reference data points; and a processormodule associated with the sensor data module and the input module andprogrammed to match reference data points with time-matched sensor datapoints to form a calibration set including at least one matched datapair; and a start-up module associated with the processor moduleprogrammed to determine the stability of the continuous analyte sensorand output information reflective of the sensor data once apredetermined level of stability has been determined.

In a sixth embodiment, a computer system is provided for initializing acontinuous analyte sensor, the system including: a sensor data receivingmodule that receives sensor data including one or more sensor datapoints from the substantially continuous analyte sensor via a receiver;a reference data receiving module for receiving reference data from areference analyte monitor, including one or more reference data points;a data matching module for forming one or more matched data pairs bymatching reference data to substantially time corresponding sensor data;a calibration set module for forming a calibration set including atleast one matched data pair; a stability determination module forevaluating the stability of the continuous analyte sensor; and aninterface control module that outputs information reflective of thesensor data once a predetermined level of stability has been determined.

In a seventh embodiment, a method for initializing a glucose sensor, themethod including: receiving sensor data from the glucose sensor,including one or more sensor data points; receiving reference data froma reference glucose monitor, including one or more reference datapoints; forming one or more matched data pairs by matching referencedata to substantially time corresponding sensor data; determiningwhether the glucose sensor has reached a predetermined level ofstability.

In an eighth embodiment, a system is provided for initializing acontinuous analyte sensor, including: a sensor data module operativelylinked to a continuous analyte sensor and configured to receive one ormore sensor data points from the sensor; a reference input moduleadapted to obtain one or more reference data points; and a processormodule associated with the sensor data module and the input module andprogrammed to match reference data points with time-matched sensor datapoints to form a calibration set including at least one matched datapair; and a stability module associated with the processor moduleprogrammed to determine the stability of the continuous analyte sensor.

In a ninth embodiment, a method is provided for evaluating clinicalacceptability of at least one of reference and sensor analyte data, themethod including: receiving a data stream from an analyte sensor,including one or more sensor data points; receiving reference data froma reference analyte monitor, including one or more reference datapoints; and evaluating the clinical acceptability at least one of thereference and sensor analyte data using substantially time correspondingreference or sensor data, wherein the at least one of the reference andsensor analyte data is evaluated for deviation from its substantiallytime corresponding reference or sensor data and clinical risk associatedwith that deviation based on the glucose value indicated by at least oneof the sensor and reference data.

In an aspect of the ninth embodiment, the method further includesproviding an output through a user interface responsive to the clinicalacceptability evaluation.

In an aspect of the ninth embodiment, the step of providing an outputincludes alerting the user based on the clinical acceptabilityevaluation.

In an aspect of the ninth embodiment, the step of providing an outputincludes altering the user interface based on the clinical acceptabilityevaluation.

In an aspect of the ninth embodiment, the step of altering the userinterface includes at least one of providing color-coded information,trend information, directional information (e.g., arrows or angledlines), and/or fail-safe information.

In an aspect of the ninth embodiment, the step of evaluating theclinical acceptability includes using one of a Clarke Error Grid, a meanabsolute difference calculation, a rate of change calculation, aconsensus grid, and a standard clinical acceptance test.

In an aspect of the ninth embodiment, the method further includesrequesting additional reference data if the clinical acceptabilityevaluation determines clinical unacceptability.

In an aspect of the ninth embodiment, the method further includesrepeating the clinical acceptability evaluation step for the additionalreference data.

In an aspect of the ninth embodiment, the method further includes a stepof matching reference data to substantially time corresponding sensordata to form a matched pair after the clinical acceptability evaluationstep.

In a tenth embodiment, a system is provided for evaluating clinicalacceptability of at least one of reference and sensor analyte data, themethod including: means for receiving a data stream from an analytesensor, a plurality of time-spaced sensor data points; means forreceiving reference data from a reference analyte monitor, including oneor more reference data points; and means for evaluating the clinicalacceptability of at least one of the reference and sensor analyte datausing substantially time corresponding reference and sensor data,wherein the at least one of the reference and sensor analyte data isevaluated for deviation from its substantially time correspondingreference or sensor data and clinical risk associated with thatdeviation based on the glucose value indicated by at least one of thesensor and reference data.

In an aspect of the tenth embodiment, the system further includes meansfor providing an output based through a user interface responsive to theclinical acceptability evaluation.

In an aspect of the tenth embodiment, the means for providing an outputincludes means for alerting the user based on the clinical acceptabilityevaluation.

In an aspect of the tenth embodiment, the means for providing an outputincludes means for altering the user interface based on the clinicalacceptability evaluation.

In an aspect of the tenth embodiment, the means for altering the userinterface includes at least one of providing color-coded information,trend information, directional information (e.g., arrows or angledlines), and/or fail-safe information.

In an aspect of the tenth embodiment, the means for evaluating theclinical acceptability includes using one of a Clarke Error Grid, a meanabsolute difference calculation, a rate of change calculation, aconsensus grid, and a standard clinical acceptance test.

In an aspect of the tenth embodiment, the system further includes meansfor requesting additional reference data if the clinical acceptabilityevaluation determines clinical unacceptability.

In an aspect of the tenth embodiment, the system further includes meansfor repeated the clinical acceptability evaluation for the additionalreference data.

In an aspect of the tenth embodiment, the system further includes meansfor matching reference data to substantially time corresponding sensordata to form a matched data pair after the clinical acceptabilityevaluation.

In an eleventh embodiment, a computer system is provided for evaluatingclinical acceptability of at least one of reference and sensor analytedata, the computer system including: a sensor data receiving module thatreceives a data stream including a plurality of time spaced sensor datapoints from a substantially continuous analyte sensor; a reference datareceiving module that receives reference data from a reference analytemonitor, including one or more reference data points; and a clinicalacceptability evaluation module that evaluates at least one of thereference and sensor analyte data using substantially time correspondingreference and sensor data, wherein the at least one of the reference andsensor analyte data is evaluated for deviation from its substantiallytime corresponding reference or sensor data and clinical risk associatedwith that deviation based on the glucose value indicated by at least oneof the sensor and reference data.

In an aspect of the eleventh embodiment, the computer system furtherincludes an interface control module that controls the user interfacebased on the clinical acceptability evaluation.

In an aspect of the eleventh embodiment, the interface control modulealerts the user based on the clinical acceptability evaluation.

In an aspect of the eleventh embodiment, the interface control modulealters the user interface based on the clinical acceptabilityevaluation.

In an aspect of the eleventh embodiment, the interface control modulealters the user interface by providing at least one of providingcolor-coded information, trend information, directional information(e.g., arrows or angled lines), and/or fail-safe information.

In an aspect of the eleventh embodiment, the clinical acceptabilityevaluation module uses one of a Clarke Error Grid, a mean absolutedifference calculation, a rate of change calculation, a consensus grid,and a standard clinical acceptance test to evaluate clinicalacceptability.

In an aspect of the eleventh embodiment, the interface control modulethat requests additional reference data if the clinical acceptabilityevaluation determines clinical unacceptability.

In an aspect of the eleventh embodiment, the interface control moduleevaluates the additional reference data using clinical acceptabilityevaluation module.

In an aspect of the eleventh embodiment, the computer system furtherincludes a data matching module that matches clinically acceptablereference data to substantially time corresponding clinically acceptablesensor data to form a matched pair.

In a twelfth embodiment, a method is provided for evaluating clinicalacceptability of at least one of reference and sensor analyte data, themethod including: receiving a data stream from an analyte sensor,including one or more sensor data points; receiving reference data froma reference analyte monitor, including one or more reference datapoints; evaluating the clinical acceptability at least one of thereference and sensor analyte data using substantially time correspondingreference and sensor data, wherein the at least one of the reference andsensor analyte data is evaluated for deviation from its substantiallytime corresponding reference or sensor data and clinical risk associatedwith that deviation based on the glucose value indicated by at least oneof the sensor and reference data; and providing an output through a userinterface responsive to the clinical acceptability evaluation.

In an thirteenth embodiment, a method is provided for evaluatingclinical acceptability of at least one of reference and sensor analytedata, the method including: receiving a data stream from an analytesensor, including one or more sensor data points; receiving referencedata from a reference analyte monitor, including one or more referencedata points; and evaluating the clinical acceptability at least one ofthe reference and sensor analyte data using substantially timecorresponding reference and sensor data, including using one of a ClarkeError Grid, a mean absolute difference calculation, a rate of changecalculation, and a consensus grid.

In an fourteenth embodiment, a computer system is provided forevaluating clinical acceptability of at least one of reference andsensor analyte data, the computer system including: a sensor data modulethat receives a data stream including a plurality of time spaced sensordata points from a substantially continuous analyte sensor; a referenceinput module that receives reference data from a reference analytemonitor, including one or more reference data points; a clinical modulethat evaluates at least one of the reference and sensor analyte datausing substantially time corresponding reference and sensor data,wherein the at least one of the reference and sensor analyte data isevaluated for deviation from its substantially time correspondingreference or sensor data and clinical risk associated with thatdeviation based on the glucose value indicated by at least one of thesensor and reference data; and an interface control module that controlsthe user interface based on the clinical acceptability evaluation.

In an fifteenth embodiment, a computer system is provided for evaluatingclinical acceptability of at least one of reference and sensor analytedata, the computer system including: a sensor data module that receivesa data stream including a plurality of time spaced sensor data pointsfrom a substantially continuous analyte sensor; a reference input modulethat receives reference data from a reference analyte monitor, includingone or more reference data points; and a clinical module that evaluatesat least one of the reference and sensor analyte data with substantiallytime corresponding reference and sensor data, wherein the clinicalmodule uses one of a Clarke Error Grid, a mean absolute differencecalculation, a rate of change calculation, a consensus grid, and astandard clinical acceptance test to evaluate clinical acceptability.

In an sixteenth embodiment, a computer system is provided for evaluatingclinical acceptability of at least one of reference and sensor analytedata, the computer system including: a sensor data module that receivesa data stream including a plurality of time spaced sensor data pointsfrom a substantially continuous analyte sensor via a receiver; areference input module that receives reference data from a referenceanalyte monitor, including one or more reference data points; and aclinical module that uses a Clarke Error Grid to evaluate the clinicalacceptability at least one of the reference and sensor analyte datausing substantially time corresponding reference and sensor data; and afail-safe module that controls the user interface responsive to theclinical module evaluating clinical unacceptability.

In an seventeenth embodiment, a method is provided for evaluatingclinical acceptability of at least one of reference and sensor glucosedata, the method including: receiving a data stream from an analytesensor, including one or more sensor data points; receiving referencedata from a reference glucose monitor, including one or more referencedata points; evaluating the clinical acceptability at least one of thereference and sensor glucose data using substantially time correspondingreference and sensor data, wherein the at least one of the reference andsensor analyte data is evaluated for deviation from its substantiallytime corresponding reference or sensor data and clinical risk associatedwith that deviation based on the glucose value indicated by at least oneof the sensor and reference data; and a fail-safe module that controlsthe user interface responsive to the clinical module evaluating clinicalunacceptability.

In an eighteenth embodiment, a method is provided for maintainingcalibration of a substantially continuous analyte sensor, the methodincluding: receiving a data stream from an analyte sensor, including oneor more sensor data points; receiving reference data from a referenceanalyte monitor, including two or more reference data points; providingat least two matched data pairs by matching reference analyte data tosubstantially time corresponding sensor data; forming a calibration setincluding the at least two matching data pairs; creating a conversionfunction based on the calibration set; converting sensor data intocalibrated data using the conversion function; subsequently obtainingone or more additional reference data points and creating one or morenew matched data pairs; evaluating the calibration set when the newmatched data pair is created, wherein evaluating the calibration setincludes at least one of 1) ensuring matched data pairs in thecalibration set span a predetermined time range, 2) ensuring matcheddata pairs in the calibration set are no older than a predeterminedvalue, 3) ensuring the calibration set has substantially distributedhigh and low matched data pairs over the predetermined time range, and4) allowing matched data pairs only within a predetermined range ofanalyte values; and subsequently modifying the calibration set if suchmodification is required by the evaluation.

In an aspect of the eighteenth embodiment, the step of evaluating thecalibration set further includes at least one of evaluating a rate ofchange of the analyte concentration, evaluating a congruence ofrespective sensor and reference data in the matched data pairs, andevaluating physiological changes.

In an aspect of the eighteenth embodiment, the step of evaluating thecalibration set includes evaluating only the new matched data pair.

In an aspect of the eighteenth embodiment, the step of evaluating thecalibration set includes evaluating all of the matched data pairs in thecalibration set and the new matched data pair.

In an aspect of the eighteenth embodiment, the step of evaluating thecalibration set includes evaluating combinations of matched data pairsfrom the calibration set and the new matched data pair.

In an aspect of the eighteenth embodiment, the step of receiving sensordata includes receiving a data stream from a long-term implantableanalyte sensor.

In an aspect of the eighteenth embodiment, the step of receiving sensordata includes receiving a data stream that has been algorithmicallysmoothed.

In an aspect of the eighteenth embodiment, the step of receiving sensordata stream includes algorithmically smoothing the data stream.

In an aspect of the eighteenth embodiment, the step of receivingreference data includes downloading reference data via a cabledconnection.

In an aspect of the eighteenth embodiment, the step of receivingreference data includes downloading reference data via a wirelessconnection.

In an aspect of the eighteenth embodiment, the step of receivingreference data from a reference analyte monitor includes receivingwithin a receiver internal communication from a reference analytemonitor integral with the receiver.

In an aspect of the eighteenth embodiment, the reference analyte monitorincludes self-monitoring of blood analyte.

In an aspect of the eighteenth embodiment, the step of creating aconversion function includes linear regression.

In an aspect of the eighteenth embodiment, the step of creating aconversion function includes non-linear regression.

In an aspect of the eighteenth embodiment, the step of forming acalibration set includes including in the calibration set between oneand six matched data pairs.

In an aspect of the eighteenth embodiment, the step of forming acalibration set includes including six matched data pairs.

In an aspect of the eighteenth embodiment, the step of forming acalibration set further includes determining a value for n, where n isgreater than one and represents the number of matched data pairs in thecalibration set.

In an aspect of the eighteenth embodiment, the step of determining avalue for n is determined as a function of the frequency of the receivedreference data points and signal strength over time.

In an aspect of the eighteenth embodiment, the method further includesdetermining a set of matching data pairs from the evaluation of thecalibration set and re-forming a calibration set.

In an aspect of the eighteenth embodiment, the method further includesrepeating the step of re-creating the conversion function using there-formed calibration set.

In an aspect of the eighteenth embodiment, the method further includesconverting sensor data into calibrated data using the re-createdconversion function.

In a nineteenth embodiment, a system is provided for maintainingcalibration of a substantially continuous analyte sensor, the systemincluding: means for receiving a data stream from an analyte sensor, aplurality of time-spaced sensor data points; means for receivingreference data from a reference analyte monitor, including two or morereference data points; means for providing two or more matched datapairs by matching reference analyte data to substantially timecorresponding sensor data; means for forming a calibration set includingat least two matched data pair; means for creating a conversion functionbased on the calibration set; means for converting sensor data intocalibrated data using the conversion function; subsequently obtainingone or more additional reference data points and creating one or morenew matched data pairs; means for evaluating the calibration set whenthe new matched data pair is created, wherein evaluating the calibrationset includes at least one of 1) ensuring matched data pairs in thecalibration set span a predetermined time range, 2) ensuring matcheddata pairs in the calibration set are no older than a predeterminedvalue, 3) ensuring the calibration set has substantially distributedhigh and low matched data pairs over the predetermined time range, and4) allowing matched data pairs only within a predetermined range ofanalyte values; and means for modifying the calibration set if suchmodification is required by the evaluation.

In an aspect of the nineteenth embodiment, the means for evaluating thecalibration set further includes at least one of means for evaluating arate of change of the analyte concentration, means for evaluating acongruence of respective sensor and reference data in matched datapairs; and means for evaluating physiological changes.

In an aspect of the nineteenth embodiment, the means for evaluating thecalibration set includes means for evaluating only the one or more newmatched data pairs.

In an aspect of the nineteenth embodiment, the means for evaluating thecalibration set includes means for evaluating all of the matched datapairs in the calibration set and the one or more new matched data pairs.

In an aspect of the nineteenth embodiment, the means for evaluating thecalibration set includes means for evaluating combinations of matcheddata pairs from the calibration set and the one or more new matched datapair.

In an aspect of the nineteenth embodiment, the means for receivingsensor data includes means for receiving sensor data from a long-termimplantable analyte sensor.

In an aspect of the nineteenth embodiment, the means for receivingsensor data includes means for receiving sensor data that has beenalgorithmically smoothed.

In an aspect of the nineteenth embodiment, the means for receivingsensor data includes means for algorithmically smoothing the receivingsensor data.

In an aspect of the nineteenth embodiment, the means for receivingreference data includes means for downloading reference data via acabled connection.

In an aspect of the nineteenth embodiment, the means for receivingreference data includes means for downloading reference data via awireless connection.

In an aspect of the nineteenth embodiment, the means for receivingreference data from a reference analyte monitor includes means forreceiving within a receiver internal communication from a referenceanalyte monitor integral with the receiver.

In an aspect of the nineteenth embodiment, the means for receivingreference data includes means for receiving from a self-monitoring ofblood analyte.

In an aspect of the nineteenth embodiment, the means for creating aconversion function includes means for performing linear regression.

In an aspect of the nineteenth embodiment, the means for creating aconversion function includes means for performing non-linear regression.

In an aspect of the nineteenth embodiment, the means for forming acalibration set includes including in the calibration set between oneand six matched data pairs.

In an aspect of the nineteenth embodiment, the means for forming acalibration set includes including in the calibration set six matcheddata pairs.

In an aspect of the nineteenth embodiment, the means for forming acalibration set further includes determining a value for n, where n isgreater than one and represents the number of matched data pairs in thecalibration set.

In an aspect of the nineteenth embodiment, the means for determining avalue for n is determined as a function of the frequency of the receivedreference data points and signal strength over time.

In an aspect of the nineteenth embodiment, the system further includesmeans for determining a set of matching data pairs from the evaluationof the calibration set and re-forming a calibration set.

In an aspect of the nineteenth embodiment, the system further includesthe means for repeating the set of creating the conversion functionusing the re-formed calibration set.

In an aspect of the nineteenth embodiment, the system further includesmeans for converting sensor data into calibrated data using there-created conversion function.

In a twentieth embodiment, a computer system is provided for maintainingcalibration of a substantially continuous analyte sensor, the computersystem including: a sensor data receiving module that receives a datastream including a plurality of time spaced sensor data points from asubstantially continuous analyte sensor; a reference data receivingmodule that receives reference data from a reference analyte monitor,including two or more reference data points; a data matching module thatforms two or more matched data pairs by matching reference data tosubstantially time corresponding sensor data; a calibration set modulethat forms a calibration set including at least two matched data pairs;a conversion function module that creates a conversion function usingthe calibration set; a sensor data transformation module that convertssensor data into calibrated data using the conversion function; and acalibration evaluation module that evaluates the calibration set whenthe new matched data pair is provided, wherein evaluating thecalibration set includes at least one of 1) ensuring matched data pairsin the calibration set span a predetermined time period, 2) ensuringmatched data pairs in the calibration set are no older than apredetermined value, 3) ensuring the calibration set has substantiallydistributed high and low matched data pairs over a predetermined timerange, and 4) allowing matched data pairs only within a predeterminedrange of analyte values, wherein the conversion function module isprogrammed to re-create the conversion function of such modification isrequired by the calibration evaluation module.

In an aspect of the twentieth embodiment, the evaluation calibrationmodule further evaluates at least one of a rate of change of the analyteconcentration, a congruence of respective sensor and reference data inmatched data pairs; and physiological changes.

In an aspect of the twentieth embodiment, the evaluation calibrationmodule evaluates only the new matched data pair.

In an aspect of the twentieth embodiment, the evaluation calibrationmodule evaluates all of the matched data pairs in the calibration setand the new matched data pair.

In an aspect of the twentieth embodiment, the evaluation calibrationmodule evaluates combinations of matched data pairs from the calibrationset and the new matched data pair.

In an aspect of the twentieth embodiment, the sensor data receivingmodule receives the data stream from a long-term implantable analytesensor.

In an aspect of the twentieth embodiment, the sensor data receivingmodule receives an algorithmically smoothed data stream.

In an aspect of the twentieth embodiment, the sensor data receivingmodule includes programming to smooth the data stream.

In an aspect of the twentieth embodiment, the reference data receivingmodule downloads reference data via a cabled connection.

In an aspect of the twentieth embodiment, the reference data receivingmodule downloads reference data via a wireless connection.

In an aspect of the twentieth embodiment, the reference data receivingmodule receives within a receiver internal communication from areference analyte monitor integral with the receiver.

In an aspect of the twentieth embodiment, the reference data receivingmodule receives reference data from a self-monitoring of blood analyte.

In an aspect of the twentieth embodiment, the conversion function moduleincludes programming that performs linear regression.

In an aspect of the twentieth embodiment, the conversion function moduleincludes programming that performs non-linear regression.

In an aspect of the twentieth embodiment, the calibration set moduleincludes in the calibration set between one and six matched data pairs.

In an aspect of the twentieth embodiment, the calibration set moduleincludes in the calibration set six matched data pairs.

In an aspect of the twentieth embodiment, the calibration set modulefurther includes programming for determining a value for n, where n isgreater than one and represents the number of matched data pairs in thecalibration set.

In an aspect of the twentieth embodiment, the programming fordetermining a value for n determines n as a function of the frequency ofthe received reference data points and signal strength over time.

In an aspect of the twentieth embodiment, data matching module furtherincludes programming to re-form the calibration set based on thecalibration evaluation.

In an aspect of the twentieth embodiment, the conversion function modulefurther includes programming to re-create the conversion function basedon the re-formed calibration set.

In an aspect of the twentieth embodiment, the sensor data transformationmodule further including programming for converting sensor data intocalibrated using the re-created conversion function.

In a twenty-first embodiment, a method is provided for maintainingcalibration of a glucose sensor, the method including: receiving a datastream from an analyte sensor, including one or more sensor data points;receiving reference data from a reference analyte monitor, including twoor more reference data points; providing at least two matched data pairsby matching reference analyte data to substantially time correspondingsensor data; forming a calibration set including the at least twomatching data pairs; creating a conversion function based on thecalibration set; subsequently obtaining one or more additional referencedata points and creating one or more new matched data pairs; andevaluating the calibration set when the new matched data pair iscreated, wherein evaluating the calibration set includes at least oneof 1) ensuring matched data pairs in the calibration set span apredetermined time range, 2) ensuring matched data pairs in thecalibration set are no older than a predetermined value, 3) ensuring thecalibration set has substantially distributed high and low matched datapairs over the predetermined time range, and 4) allowing matched datapairs only within a predetermined range of analyte values.

In a twenty-second embodiment, a computer system is provided formaintaining calibration of a glucose sensor, the computer systemincluding: a sensor data module that receives a data stream including aplurality of time spaced sensor data points from a substantiallycontinuous analyte sensor; a reference input module that receivesreference data from a reference analyte monitor, including two or morereference data points; a processor module that forms two or more matcheddata pairs by matching reference data to substantially timecorresponding sensor data and subsequently forms a calibration setincluding the two or more matched data pairs; and a calibrationevaluation module that evaluates the calibration set when the newmatched data pair is provided, wherein evaluating the calibration setincludes at least one of 1) ensuring matched data pairs in thecalibration set span a predetermined time period, 2) ensuring matcheddata pairs in the calibration set are no older than a predeterminedvalue, 3) ensuring the calibration set has substantially distributedhigh and low matched data pairs over a predetermined time range, and 4)allowing matched data pairs only within a predetermined range of analytevalues, wherein the conversion function module is programmed tore-create the conversion function of such modification is required bythe calibration evaluation module.

In a twenty-third embodiment, a method is provided for evaluating thequality of a calibration of an analyte sensor, the method including:receiving a data stream from an analyte sensor, including one or moresensor data points; receiving reference data from a reference analytemonitor, including two or more reference data points; providing at leasttwo matched data pairs by matching reference analyte data tosubstantially time corresponding sensor data; forming a calibration setincluding the at least two matching data pairs; creating a conversionfunction based on the calibration set; receiving additional sensor datafrom the analyte sensor; converting sensor data into calibrated datausing the conversion function; and evaluating the quality of thecalibration set using a data association function.

In an aspect of the twenty-third embodiment, the step of receivingsensor data includes receiving a data stream that has beenalgorithmically smoothed.

In an aspect of the twenty-third embodiment, the step of receivingsensor data includes algorithmically smoothing the data stream.

In an aspect of the twenty-third embodiment, the step of receivingsensor data includes receiving sensor data from a substantiallycontinuous glucose sensor.

In an aspect of the twenty-third embodiment, the step of receivingsensor data includes receiving sensor data from an implantable glucosesensor.

In an aspect of the twenty-third embodiment, the step of receivingsensor data includes receiving sensor data from a subcutaneouslyimplantable glucose sensor.

In an aspect of the twenty-third embodiment, the step of receivingreference data includes receiving reference data from a self-monitoringblood glucose test.

In an aspect of the twenty-third embodiment, the step of receivingreference data includes downloading reference data via a cabledconnection.

In an aspect of the twenty-third embodiment, the step of receivingreference data includes downloading reference data via a wirelessconnection.

In an aspect of the twenty-third embodiment, the step of receivingreference data from a reference analyte monitor includes receivingwithin a receiver internal communication from a reference analytemonitor integral with the receiver.

In an aspect of the twenty-third embodiment, the step of evaluating thequality of the calibration set based on a data association functionincludes performing one of linear regression, non-linear regression,rank correlation, least mean square fit, mean absolute deviation, andmean absolute relative difference.

In an aspect of the twenty-third embodiment, the step of evaluating thequality of the calibration set based on a data association functionincludes performing linear least squares regression.

In an aspect of the twenty-third embodiment, the step of evaluating thequality of the calibration set based on a data association functionincludes setting a threshold of data association.

In an aspect of the twenty-third embodiment, the step of evaluating thequality of the calibration set based on data association includesperforming linear least squares regression and wherein the step ofsetting a threshold hold includes an R-value threshold of 0.79.

In an aspect of the twenty-third embodiment, the method further includesproviding an output to a user interface responsive to the quality of thecalibration set.

In an aspect of the twenty-third embodiment, the step of providing anoutput includes displaying analyte values to a user dependent upon thequality of the calibration.

In an aspect of the twenty-third embodiment, the step of providing anoutput includes alerting the dependent upon the quality of thecalibration.

In an aspect of the twenty-third embodiment, the step of providing anoutput includes altering the user interface dependent upon the qualityof the calibration.

In an aspect of the twenty-third embodiment, the step of providing anoutput includes at least one of providing color-coded information, trendinformation, directional information (e.g., arrows or angled lines),and/or fail-safe information.

In a twenty-fourth embodiment, a system is provided for evaluating thequality of a calibration of an analyte sensor, the system including:means for receiving a data stream from an analyte sensor, a plurality oftime-spaced sensor data points; means for receiving reference data froma reference analyte monitor, including two or more reference datapoints; means for providing two or more matched data pairs by matchingreference analyte data to substantially time corresponding sensor data;means for forming a calibration set including at least two matched datapair; means for creating a conversion function based on the calibrationset; means for converting sensor data into calibrated data using theconversion function; means for evaluating the quality of the calibrationset based on a data association function.

In an aspect of the twenty-fourth embodiment, the means for receivingsensor data includes means for receiving sensor data that has beenalgorithmically smoothed.

In an aspect of the twenty-fourth embodiment, the means for receivingsensor data includes means for algorithmically smoothing the receivingsensor data.

In an aspect of the twenty-fourth embodiment, the means for receivingsensor data includes means for receiving sensor data from substantiallycontinuous glucose sensor.

In an aspect of the twenty-fourth embodiment, the means for receivingsensor data includes means for receiving sensor data from an implantableglucose sensor.

In an aspect of the twenty-fourth embodiment, the means for receivingsensor data includes means for receiving sensor data from subcutaneouslyimplantable glucose sensor.

In an aspect of the twenty-fourth embodiment, the means for receivingreference data includes means for receiving reference data from aself-monitoring blood glucose test.

In an aspect of the twenty-fourth embodiment, the means for receivingreference data includes means for downloading reference data via acabled connection.

In an aspect of the twenty-fourth embodiment, the means for receivingreference data includes means for downloading reference data via awireless connection.

In an aspect of the twenty-fourth embodiment, the means for receivingreference data from a reference analyte monitor includes means forreceiving within a receiver internal communication from a referenceanalyte monitor integral with the receiver.

In an aspect of the twenty-fourth embodiment, the means for evaluatingthe quality of the calibration set includes means for performing one oflinear regression, non-linear regression, rank correlation, least meansquare fit, mean absolute deviation, and mean absolute relativedifference.

In an aspect of the twenty-fourth embodiment, the means for evaluatingthe quality of the calibration set includes means for performing linearleast squares regression.

In an aspect of the twenty-fourth embodiment, the means for evaluatingthe quality of the calibration set includes means for setting athreshold of data association.

In an aspect of the twenty-fourth embodiment, the means for evaluatingthe quality of the calibration set includes means for performing linearleast squares regression and wherein the means for setting a thresholdhold includes an R-value threshold of 0.71.

In an aspect of the twenty-fourth embodiment, the system furtherincludes means for providing an output to a user interface responsive tothe quality of the calibration set.

In an aspect of the twenty-fourth embodiment, the means for providing anoutput includes means for displaying analyte values to a user dependentupon the quality of the calibration.

In an aspect of the twenty-fourth embodiment, the means for providing anoutput includes means for alerting the dependent upon the quality of thecalibration.

In an aspect of the twenty-fourth embodiment, the means for providing anoutput includes means for altering the user interface dependent upon thequality of the calibration.

In an aspect of the twenty-fourth embodiment, the means for providing anoutput includes at least one of providing color-coded information, trendinformation, directional information (e.g., arrows or angled lines),and/or fail-safe information.

In a twenty-fifth embodiment, a computer system is provided forevaluating the quality of a calibration of an analyte sensor, thecomputer system including: a sensor data receiving module that receivesa data stream including a plurality of time spaced sensor data pointsfrom a substantially continuous analyte sensor; a reference datareceiving module that receives reference data from a reference analytemonitor, including two or more reference data points; a data matchingmodule that forms two or more matched data pairs by matching referencedata to substantially time corresponding sensor data; a calibration setmodule that forms a calibration set including at least two matched datapairs; a conversion function module that creates a conversion functionusing the calibration set; a sensor data transformation module thatconverts sensor data into calibrated data using the conversion function;and a quality evaluation module that evaluates the quality of thecalibration set based on a data association function.

In an aspect of the twenty-fifth embodiment, the sensor data receivingmodule receives sensor data that has been algorithmically smoothed.

In an aspect of the twenty-fifth embodiment, the computer system furtherincludes a data smoothing module that algorithmically smoothes sensordata received from the sensor data receiving module.

In an aspect of the twenty-fifth embodiment, the sensor data receivingmodule is adapted to receive sensor data from substantially continuousglucose sensor.

In an aspect of the twenty-fifth embodiment, the sensor data receivingmodule is adapted to receive sensor data from an implantable glucosesensor.

In an aspect of the twenty-fifth embodiment, the sensor data receivingmodule is adapted to receive sensor data from subcutaneously implantableglucose sensor.

In an aspect of the twenty-fifth embodiment, the reference datareceiving module is adapted to receive reference data from aself-monitoring blood glucose test.

In an aspect of the twenty-fifth embodiment, the reference datareceiving module is adapted to download reference data via a cabledconnection.

In an aspect of the twenty-fifth embodiment, the reference datareceiving module is adapted to download reference data via a wirelessconnection.

In an aspect of the twenty-fifth embodiment, the reference datareceiving module is adapted to receive reference data from a referenceanalyte monitor integral with the receiver.

In an aspect of the twenty-fifth embodiment, the quality evaluationmodule performs one of linear regression, non-linear regression, rankcorrelation, least mean square fit, mean absolute deviation, and meanabsolute relative difference to evaluate calibration set quality.

In an aspect of the twenty-fifth embodiment, the quality evaluationmodule performs linear least squares regression.

In an aspect of the twenty-fifth embodiment, the quality evaluationmodule sets a threshold for the data association function.

In an aspect of the twenty-fifth embodiment, the quality evaluationmodule performs linear least squares regression and wherein thethreshold of the data association function includes an R-value thresholdof at least 0.79.

In an aspect of the twenty-fifth embodiment, the computer system furtherincludes an interface control module that controls the user interfacebased on the quality of the calibration set.

In an aspect of the twenty-fifth embodiment, the interface controlmodule displays analyte values to a user dependent upon the quality ofthe calibration set.

In an aspect of the twenty-fifth embodiment, the interface controlmodule alerts the user based upon the quality of the calibration set.

In an aspect of the twenty-fifth embodiment, the interface controlmodule alters the user interface based upon the quality of thecalibration set.

In an aspect of the twenty-fifth embodiment, the interface controlmodule provides at least one of color-coded information, trendinformation, directional information (e.g., arrows or angled lines),and/or fail-safe information.

In a twenty-sixth embodiment, a method is provided for evaluating thequality of a calibration of an analyte sensor, the method including:receiving a data stream from an analyte sensor, including one or moresensor data points; receiving reference data from a reference analytemonitor, including two or more reference data points; providing at leasttwo matched data pairs by matching reference analyte data tosubstantially time corresponding sensor data; forming a calibration setincluding the at least two matching data pairs; creating a conversionfunction based on the calibration set; receiving additional sensor datafrom the analyte sensor; converting sensor data into calibrated datausing the conversion function; and evaluating the quality of thecalibration set based on a data association function selected from thegroup consisting of linear regression, non-linear regression, rankcorrelation, least mean square fit, mean absolute deviation, and meanabsolute relative difference.

In a twenty-seventh embodiment, a method is provided for evaluating thequality of a calibration of an analyte sensor, the method including:receiving a data stream from an analyte sensor, including one or moresensor data points; receiving reference data from a reference analytemonitor, including two or more reference data points; providing at leasttwo matched data pairs by matching reference analyte data tosubstantially time corresponding sensor data; forming a calibration setincluding the at least two matching data pairs; creating a conversionfunction based on the calibration set; receiving additional sensor datafrom the analyte sensor; converting sensor data into calibrated datausing the conversion function; evaluating the quality of the calibrationset using a data association function; and providing an output to a userinterface responsive to the quality of the calibration set.

In a twenty-eighth embodiment, a computer system is provided forevaluating the quality of a calibration of an analyte sensor, thecomputer system including: a sensor data module that receives a datastream including a plurality of time spaced sensor data points from asubstantially continuous analyte sensor; a reference input module thatreceives reference data from a reference analyte monitor, including twoor more reference data points; a processor module that forms two or morematched data pairs by matching reference data to substantially timecorresponding sensor data and subsequently forms a calibration setincluding the two or more matched data pairs; and a conversion functionmodule that creates a conversion function using the calibration set; asensor data transformation module that converts sensor data intocalibrated data using the conversion function; a quality evaluationmodule that evaluates the quality of the calibration set based on a dataassociation selected from the group consisting of linear regression,non-linear regression, rank correlation, least mean square fit, meanabsolute deviation, and mean absolute relative difference.

In a twenty-ninth embodiment, a computer system is provided forevaluating the quality of a calibration of an analyte sensor, thecomputer system including: a sensor data module that receives a datastream including a plurality of time spaced sensor data points from asubstantially continuous analyte sensor; a reference input module thatreceives reference data from a reference analyte monitor, including twoor more reference data points; a processor module that forms two or morematched data pairs by matching reference data to substantially timecorresponding sensor data and subsequently forms a calibration setincluding the two or more matched data pairs; and a conversion functionmodule that creates a conversion function using the calibration set; asensor data transformation module that converts sensor data intocalibrated data using the conversion function; a quality evaluationmodule that evaluates the quality of the calibration set based on dataassociation; and a fail-safe module that controls the user interfacebased on the quality of the calibration set.

In a thirtieth embodiment, a method is provided for evaluating thequality of a calibration of a glucose sensor, the method including:receiving sensor data from a glucose sensor, including one or moresensor data points; receiving reference data from a reference glucosemonitor, including one or more reference data points; providing one ormore matched data pairs by matched reference glucose data tosubstantially time corresponding sensor data; forming a calibration setincluding at least one matched data pair; and evaluating the quality ofthe calibration set based on data association.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exploded perspective view of a glucose sensor in oneembodiment.

FIG. 2 is a block diagram that illustrates the sensor electronics in oneembodiment.

FIG. 3 is a graph that illustrates data smoothing of a raw data signalin one embodiment.

FIGS. 4A to 4D are schematic views of a receiver in first, second,third, and fourth embodiments, respectively.

FIG. 5 is a block diagram of the receiver electronics in one embodiment.

FIG. 6 is a flow chart that illustrates the initial calibration and dataoutput of the sensor data in one embodiment.

FIG. 7 is a graph that illustrates a regression performed on acalibration set to obtain a conversion function in one exemplaryembodiment.

FIG. 8 is a flow chart that illustrates the process of evaluating theclinical acceptability of reference and sensor data in one embodiment.

FIG. 9 is a graph of two data pairs on a Clarke Error Grid to illustratethe evaluation of clinical acceptability in one exemplary embodiment.

FIG. 10 is a flow chart that illustrates the process of evaluation ofcalibration data for best calibration based on inclusion criteria ofmatched data pairs in one embodiment.

FIG. 11 is a flow chart that illustrates the process of evaluating thequality of the calibration in one embodiment.

FIGS. 12A and 12B are graphs that illustrate an evaluation of thequality of calibration based on data association in one exemplaryembodiment using a correlation coefficient.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

The following description and examples illustrate some exemplaryembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain exemplary embodiment should not be deemed tolimit the scope of the present invention.

Definitions

In order to facilitate an understanding of the disclosed invention, anumber of terms are defined below.

The term “analyte,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, to refer to a substanceor chemical constituent in a biological fluid (for example, blood,interstitial fluid, cerebral spinal fluid, lymph fluid or urine) thatcan be analyzed. Analytes may include naturally occurring substances,artificial substances, metabolites, and/or reaction products. In someembodiments, the analyte for measurement by the sensor heads, devices,and methods is analyte. However, other analytes are contemplated aswell, including but not limited to acarboxyprothrombin; acylcarnitine;adenine phosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; camitine; camosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase,hemoglobinopathies, A, S, C, E, D-Punjab, beta-thalassemia, hepatitis Bvirus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD,RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol);desbutylhalofantrine; dihydropteridine reductase; diptheria/tetanusantitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D;fatty acids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I; 17alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenyloin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins and hormones naturally occurring in blood or interstitialfluids may also constitute analytes in certain embodiments. The analytemay be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte may be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbituates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body may also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The terms “operably connected” and “operably linked,” as used herein,are broad terms and are used in their ordinary sense, including, withoutlimitation, one or more components being linked to another component(s)in a manner that allows transmission of signals between the components,e.g., wired or wirelessly. For example, one or more electrodes may beused to detect the amount of analyte in a sample and convert thatinformation into a signal; the signal may then be transmitted to anelectronic circuit means. In this case, the electrode is “operablylinked” to the electronic circuitry.

The term “EEPROM,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, electrically erasableprogrammable read-only memory, which is user-modifiable read-only memory(ROM) that can be erased and reprogrammed (e.g., written to) repeatedlythrough the application of higher than normal electrical voltage.

The term “SRAM,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, static random accessmemory (RAM) that retains data bits in its memory as long as power isbeing supplied.

The term “A/D Converter,” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, hardware thatconverts analog signals into digital signals.

The term “microprocessor,” as used herein, is a broad term and is usedin its ordinary sense, including, without limitation a computer systemor processor designed to perform arithmetic and logic operations usinglogic circuitry that responds to and processes the basic instructionsthat drive a computer.

The term “RF transceiver,” as used herein, is a broad term and is usedin its ordinary sense, including, without limitation, a radio frequencytransmitter and/or receiver for transmitting and/or receiving signals.

The term “jitter” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, uncertainty orvariability of waveform timing, which may be cause by ubiquitous noisecaused by a circuit and/or environmental effects; jitter can be seen inamplitude, phase timing, or the width of the signal pulse.

The term “raw data signal,” as used herein, is a broad term and is usedin its ordinary sense, including, without limitation, an analog ordigital signal directly related to the measured analyte from the analytesensor. In one example, the raw data signal is digital data in “counts”converted by an A/D converter from an analog signal (e.g., voltage oramps) representative of an analyte concentration.

The term “counts,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, a unit of measurement ofa digital signal. In one example, a raw data signal measured in countsis directly related to a voltage (converted by an A/D converter), whichis directly related to current.

The term “analyte sensor,” as used herein, is a broad term and is usedin its ordinary sense, including, without limitation, any mechanism(e.g., enzymatic or non-enzymatic) by which analyte can be quantified.For example, some embodiments utilize a membrane that contains glucoseoxidase that catalyzes the conversion of oxygen and glucose to hydrogenperoxide and gluconate:Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O₂ and the product H₂O₂, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The term “host,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, mammals, particularlyhumans.

The term “matched data pairs”, as used herein, is a broad term and isused in its ordinary sense, including, without limitation, referencedata (e.g., one or more reference analyte data points) matched withsubstantially time corresponding sensor data (e.g., one or more sensordata points).

The term “Clarke Error Grid”, as used herein, is a broad term and isused in its ordinary sense, including, without limitation, an error gridanalysis, which evaluates the clinical significance of the differencebetween a reference glucose value and a sensor generated glucose value,taking into account 1) the value of the reference glucose measurement,2) the value of the sensor glucose measurement, 3) the relativedifference between the two values, and 4) the clinical significance ofthis difference. See Clarke et al., “Evaluating Clinical Accuracy ofSystems for Self-Monitoring of Blood Glucose”, Diabetes Care, Volume 10,Number 5, September-October 1987, which is incorporated by referenceherein in its entirety.

The term “Consensus Error Grid”, as used herein, is a broad term and isused in its ordinary sense, including, without limitation, an error gridanalysis that assigns a specific level of clinical risk to any possibleerror between two time corresponding glucose measurements. The ConsensusError Grid is divided into zones signifying the degree of risk posed bythe deviation. See Parkes et al., “A New Consensus Error Grid toEvaluate the Clinical Significance of Inaccuracies in the Measurement ofBlood Glucose”, Diabetes Care, Volume 23, Number 8, August 2000, whichis incorporated by reference herein in its entirety.

The term “clinical acceptability”, as used herein, is a broad term andis used in its ordinary sense, including, without limitation,determination of the risk of inaccuracies to a patient. Clinicalacceptability considers a deviation between time corresponding glucosemeasurements (e.g., data from a glucose sensor and data from a referenceglucose monitor) and the risk (e.g., to the decision making of adiabetic patient) associated with that deviation based on the glucosevalue indicated by the sensor and/or reference data. One example ofclinical acceptability may be 85% of a given set of measured analytevalues within the “A” and “B” region of a standard Clarke Error Gridwhen the sensor measurements are compared to a standard referencemeasurement.

The term “R-value,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, one conventional way ofsummarizing the correlation of data; that is, a statement of whatresiduals (e.g., root mean square deviations) are to be expected if thedata are fitted to a straight line by the a regression.

The term “data association” and “data association function,” as usedherein, are a broad terms and are used in their ordinary sense,including, without limitation, a statistical analysis of data andparticularly its correlation to, or deviation from, from a particularcurve. A data association function is used to show data association. Forexample, the data that forms that calibration set as described hereinmay be analyzed mathematically to determine its correlation to, ordeviation from, a curve (e.g., line or set of lines) that defines theconversion function; this correlation or deviation is the dataassociation. A data association function is used to determine dataassociation. Examples of data association functions include, but are notlimited to, linear regression, non-linear mapping/regression, rank(e.g., non-parametric) correlation, least mean square fit, mean absolutedeviation (MAD), mean absolute relative difference. In one such example,the correlation coefficient of linear regression is indicative of theamount of data association of the calibration set that forms theconversion function, and thus the quality of the calibration.

The term “quality of calibration” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, thestatistical association of matched data pairs in the calibration setused to create the conversion function. For example, an R-value may becalculated for a calibration set to determine its statistical dataassociation, wherein an R-value greater than 0.79 determines astatistically acceptable calibration quality, while an R-value less than0.79 determines statistically unacceptable calibration quality.

The term “substantially” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, being largely but notnecessarily wholly that which is specified.

The term “congruence” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, the quality or state ofagreeing, coinciding, or being concordant. In one example, congruencemay be determined using rank correlation.

The term “concordant” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, being in agreement orharmony, and/or free from discord.

The phrase “continuous (or continual) analyte sensing,” as used herein,is a broad term and is used in its ordinary sense, including, withoutlimitation, the period in which monitoring of analyte concentration iscontinuously, continually, and or intermittently (but regularly)performed, for example, about every 5 to 10 minutes.

The term “sensor head,” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, the region of amonitoring device responsible for the detection of a particular analyte.In one example, a sensor head comprises a non-conductive body, a workingelectrode (anode), a reference electrode and a counter electrode(cathode) passing through and secured within the body forming anelectrochemically reactive surface at one location on the body and anelectronic connective means at another location on the body, and asensing membrane affixed to the body and covering the electrochemicallyreactive surface. The counter electrode has a greater electrochemicallyreactive surface area than the working electrode. During generaloperation of the sensor a biological sample (e.g., blood or interstitialfluid) or a portion thereof contacts (directly or after passage throughone or more membranes or domains) an enzyme (e.g., glucose oxidase); thereaction of the biological sample (or portion thereof) results in theformation of reaction products that allow a determination of the analyte(e.g., glucose) level in the biological sample. In some embodiments, thesensing membrane further comprises an enzyme domain (e.g., and enzymelayer), and an electrolyte phase (e.g., a free-flowing liquid phasecomprising an electrolyte-containing fluid described further below).

The term “electrochemically reactive surface,” as used herein, is abroad term and is used in its ordinary sense, including, withoutlimitation, the surface of an electrode where an electrochemicalreaction takes place. In the case of the working electrode, the hydrogenperoxide produced by the enzyme catalyzed reaction of the analyte beingdetected creates a measurable electronic current (e.g., detection ofanalyte utilizing analyte oxidase produces H₂O₂ peroxide as a byproduct, H₂O₂ reacts with the surface of the working electrode producingtwo protons (2H⁺), two electrons (2e⁻) and one molecule of oxygen (O₂)which produces the electronic current being detected). In the case ofthe counter electrode, a reducible species, e.g., O₂ is reduced at theelectrode surface in order to balance the current being generated by theworking electrode.

The term “electronic connection,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, anyelectronic connection known to those in the art that may be utilized tointerface the sensor head electrodes with the electronic circuitry of adevice such as mechanical (e.g., pin and socket) or soldered.

The term “sensing membrane,” as used herein, is a broad term and is usedin its ordinary sense, including, without limitation, a permeable orsemi-permeable membrane that may be comprised of two or more domains andconstructed of materials of a few microns thickness or more, which arepermeable to oxygen and may or may not be permeable to an analyte ofinterest. In one example, the sensing membrane comprises an immobilizedglucose oxidase enzyme, which enables an electrochemical reaction tooccur to measure a concentration of glucose.

The term “biointerface membrane,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, a permeablemembrane that may be comprised of two or more domains and constructed ofmaterials of a few microns thickness or more, which may be placed overthe sensor body to keep host cells (e.g., macrophages) from gainingproximity to, and thereby damaging, the sensing membrane or forming abarrier cell layer and interfering with the transport of analyte acrossthe tissue-device interface.

In the disclosure which follows, the following abbreviations apply: Eqand Eqs (equivalents); mEq (milliequivalents); M (molar); mM(millimolar) μM (micromolar); N (Normal); mol (moles); mmol(millimoles); μmol (micromoles); nmol (nanomoles); g (grams); mg(milligrams); μg (micrograms); Kg (kilograms); L (liters); mL(milliliters); dL (deciliters); μL (microliters); cm (centimeters); mm(millimeters); μm (micrometers); nm (nanometers); h and hr (hours); min.(minutes); s and sec. (seconds); ° C. (degrees Centigrade).

Overview

The preferred embodiments relate to the use of an analyte sensor thatmeasures a concentration of analyte of interest or a substanceindicative of the concentration or presence of the analyte. In someembodiments, the sensor is a continuous device, for example asubcutaneous, transdermal, or intravascular device. In some embodiments,the device may analyze a plurality of intermittent blood samples. Theanalyte sensor may use any method of analyte-sensing, includingenzymatic, chemical, physical, electrochemical, spectrophotometric,polarimetric, calorimetric, radiometric, or the like.

The analyte sensor uses any known method, including invasive, minimallyinvasive, and non-invasive sensing techniques, to provide an outputsignal indicative of the concentration of the analyte of interest. Theoutput signal is typically a raw signal that is used to provide a usefulvalue of the analyte of interest to a user, such as a patient orphysician, who may be using the device. Accordingly, appropriatesmoothing, calibration, and evaluation methods may be applied to the rawsignal and/or system as a whole to provide relevant and acceptableestimated analyte data to the user.

Sensor

The analyte sensor useful with the preferred embodiments may be anydevice capable of measuring the concentration of an analyte of interest.One exemplary embodiment is described below, which utilizes animplantable glucose sensor. However, it should be understood that thedevices and methods described herein may be applied to any devicecapable of detecting a concentration of analyte of and providing anoutput signal that represents the concentration of the analyte.

FIG. 1 is an exploded perspective view of a glucose sensor in oneembodiment. The implantable glucose sensor 10 utilizes amperometricelectrochemical sensor technology to measure glucose. In this exemplaryembodiment, a body 12 and a head 14 house electrodes 16 and sensorelectronics, which are described in more detail with reference to FIG.2. Three electrodes 16 are operably connected to the sensor electronics(FIG. 2) and are covered by a sensing membrane 17 and a biointerfacemembrane 18, which are attached by a clip 19. In alternativeembodiments, the number of electrodes may be less than or greater thanthree.

The three electrodes 16, which protrude through the head 14, including aplatinum working electrode, a platinum counter electrode, and asilver/silver chloride reference electrode. The top ends of theelectrodes are in contact with an electrolyte phase (not shown), whichis a free-flowing fluid phase disposed between the sensing membrane andthe electrodes. The sensing membrane 17 includes an enzyme, e.g.,glucose oxidase, which covers the electrolyte phase. In turn, thebiointerface membrane 18 covers the sensing membrane 17 and serves, atleast in part, to protect the sensor from external forces that mayresult in environmental stress cracking of the sensing membrane 17.

In the illustrated embodiment, the counter electrode is provided tobalance the current generated by the species being measured at theworking electrode. In the case of a glucose oxidase based glucosesensor, the species being measured at the working electrode is H₂O₂.Glucose oxidase catalyzes the conversion of oxygen and glucose tohydrogen peroxide and gluconate according to the following reaction:Glucose+O₂→Gluconate+H₂O₂

The change in H₂O₂ can be monitored to determine glucose concentrationbecause for each glucose molecule metabolized, there is a proportionalchange in the product H₂O₂. Oxidation of H₂O₂ by the working electrodeis balanced by reduction of ambient oxygen, enzyme generated H₂O₂, orother reducible species at the counter electrode. The H₂O₂ produced fromthe glucose oxidase reaction further reacts at the surface of workingelectrode and produces two protons (2H⁺), two electrons (2e⁻), and oneoxygen molecule (O₂) (See, e.g., Fraser, D. M. “An Introduction to Invivo Biosensing: Progress and problems.” In “Biosensors and the Body,”D. M. Fraser, ed., 1997, pp. 1-56 John Wiley and Sons, New York.)

In one embodiment, a potentiostat is used to measure the electrochemicalreaction(s) at the electrode(s) (see FIG. 2). The potentiostat applies aconstant potential between the working and reference electrodes toproduce a current value. The current that is produced at the workingelectrode (and flows through the circuitry to the counter electrode) isproportional to the diffusional flux of H₂O₂. Accordingly, a raw signalmay be produced that is representative of the concentration of glucosein the users body, and therefore may be utilized to estimate ameaningful glucose value, such as described elsewhere herein.

One problem of enzymatic glucose sensors such as described above is thenon-glucose reaction rate-limiting phenomenon. For example, if oxygen isdeficient, relative to the amount of glucose, then the enzymaticreaction will be limited by oxygen rather than glucose. Consequently,the output signal will be indicative of the oxygen concentration ratherthan the glucose concentration.

FIG. 2 is a block diagram that illustrates the sensor electronics in oneembodiment. In this embodiment, the potentiostat 20 is shown, which isoperatively connected to electrodes 16 (FIG. 1) to obtain a currentvalue, and includes a resistor (not shown) that translates the currentinto voltage. An A/D converter 21 digitizes the analog signal intocounts for processing. Accordingly, the resulting raw data signal incounts is directly related to the current measured by the potentiostat20.

A microprocessor 22 is the central control unit that houses EEPROM 23and SRAM 24, and controls the processing of the sensor electronics. Itmay be noted that alternative embodiments utilize a computer systemother than a microprocessor to process data as described herein. In somealternative embodiments, an application-specific integrated circuit(ASIC) may be used for some or all the sensor's central processing. TheEEPROM 23 provides semi-permanent storage of data, storing data such assensor ID and necessary programming to process data signals (e.g.,programming for data smoothing such as described below). The SRAM 24 isused for the system's cache memory, for example for temporarily storingrecent sensor data.

A battery 25 is operatively connected to the microprocessor 22 andprovides the necessary power for the sensor. In one embodiment, thebattery is a Lithium Manganese Dioxide battery, however anyappropriately sized and powered battery may be used (e.g., AAA,Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metal hydride,Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, orhermetically-sealed). In some embodiments, a plurality of batteries maybe used to power the system. A Quartz Crystal 26 is operativelyconnected to the microprocessor 22 and maintains system time for thecomputer system as a whole.

An RF Transceiver 27 is operably connected to the microprocessor 22 andtransmits the sensor data from the sensor to a receiver (see FIGS. 4 and5). Although an RF transceiver is shown here, other embodiments includea wired rather than wireless connection to the receiver. In yet otherembodiments, the receiver is transcutaneously powered via an inductivecoupling, for example. A quartz crystal 28 provides the system time forsynchronizing the data transmissions from the RF transceiver. It may benoted that the transceiver 27 may be substituted for a transmitter inone embodiment.

Data Smoothing

Typically, an analyte sensor produces a raw data signal that isindicative of the analyte concentration of a user, such as described inmore detail with reference to FIGS. 1 and 2, above. However, it is wellknown that the above described glucose sensor is only one example of anabundance of analyte sensors that are able to provide a raw data signaloutput indicative of the concentration of the analyte of interest. Thus,it should be understood that the devices and methods of the preferredembodiments, including data smoothing, calibration, evaluation, andother data processing, may be applied to raw data obtained from anyanalyte sensor capable of producing a output signal.

It has been found that raw data signals received from an analyte sensorinclude signal noise, which degrades the quality of the data. Thus, ithas been known to use smoothing algorithms help improve thesignal-to-noise ratio in the sensor by reducing signal jitter, forexample. One example of a conventional data smoothing algorithms includefinite impulse response filter (FIR), which is particularly suited forreducing high-frequency noise (see Steil et al. U.S. Pat. No.6,558,351). Other analyte sensors have utilized heuristic and movingaverage type algorithms to accomplish data smoothing of signal jitter indata signals, for example.

It is advantageous to also reduce signal noise by attenuating transient,low frequency, non-analyte related signal fluctuations (e.g., transientischemia and/or long transient periods of postural effects thatinterfere with sensor function due to lack of oxygen and/or otherphysiological effects).

In one embodiment, this attenuation of transient low frequencynon-analyte related signal noise is accomplished using a recursivefilter. In contrast to conventional non-recursive (e.g., FIR) filters inwhich each computation uses new input data sets, a recursive filter isan equation that uses moving averages as inputs; that is, a recursivefilter includes previous averages as part of the next filtered output.Recursive filters are advantageous at least in part due to theircomputational efficiency.

FIG. 3 is a graph that illustrates data smoothing of a raw data signalin one embodiment. In this embodiment, the recursive filter isimplemented as a digital infinite impulse response filter (IIR) filter,wherein the output is computed using 6 additions and 7 multiplies asshown in the following equation:

${y(n)} = \frac{\begin{matrix}{{a_{0}*{x(n)}} + {a_{1}*{x\left( {n - 1} \right)}} + {a_{2}*{x\left( {n - 2} \right)}} + {a_{3}*{x\left( {n - 3} \right)}} -} \\{{b_{1}*{y\left( {n - 1} \right)}} - {b_{2}*{y\left( {n - 2} \right)}} - {b_{3}*{y\left( {n - 3} \right)}}}\end{matrix}}{b_{0}}$This polynomial equation includes coefficients that are dependent onsample rate and frequency behavior of the filter. In this exemplaryembodiment, frequency behavior passes low frequencies up to cyclelengths of 40 minutes, and is based on a 30 second sample rate.

In some embodiments, data smoothing may be implemented in the sensor andthe smoothed data transmitted to a receiver for additional processing.In other embodiments, raw data may be sent from the sensor to a receiverfor data smoothing and additional processing therein. In yet otherembodiments, the sensor is integral with the receiver and therefore notransmission of data is required.

In one exemplary embodiment, wherein the sensor is an implantableglucose sensor, data smoothing is performed in the sensor to ensure acontinuous stream of data. In alternative embodiments, data smoothingmay be transmitted from the sensor to the receiver, and the datasmoothing performed at the receiver; it may be noted however that theremay be a risk of transmit-loss in the radio transmission from the sensorto the receiver when the transmission is wireless. For example, inembodiments wherein a sensor is implemented in vivo, the raw sensorsignal may be more consistent within the sensor (in vivo) than the rawsignal transmitted to a source (e.g., receiver) outside the body (e.g.,if a patient were to take the receiver off to shower, communicationbetween the sensor and receiver may be lost and data smoothing in thereceiver would halt accordingly.) Consequently, it may be noted that amultiple point data loss in the filter may take, for example, anywherefrom 25 to 40 minutes for the smoothed data to recover to where it wouldhave been had there been no data loss.

Receiver

FIGS. 4A to 4D are schematic views of a receiver in first, second,third, and fourth embodiments, respectively. A receiver 40 comprisessystems necessary to receive, process, and display sensor data from ananalyte sensor, such as described elsewhere herein. Particularly, thereceiver 40 may be a pager-sized device, for example, and comprise auser interface that has a plurality of buttons 42 and a liquid crystaldisplay (LCD) screen 44, and which may include a backlight. In someembodiments the user interface may also include a keyboard, a speaker,and a vibrator such as described with reference to FIG. 5.

FIG. 4A illustrates a first embodiment wherein the receiver shows anumeric representation of the estimated analyte value on its userinterface, which is described in more detail elsewhere herein.

FIG. 4B illustrates a second embodiment wherein the receiver shows anestimated glucose value and one hour of historical trend data on itsuser interface, which is described in more detail elsewhere herein.

FIG. 4C illustrates a third embodiment wherein the receiver shows anestimated glucose value and three hours of historical trend data on itsuser interface, which is described in more detail elsewhere herein.

FIG. 4D illustrates a fourth embodiment wherein the receiver shows anestimated glucose value and nine hours of historical trend data on itsuser interface, which is described in more detail elsewhere herein.

In some embodiments a user is able to toggle through some or all of thescreens shown in FIGS. 4A to 4D using a toggle button on the receiver.In some embodiments, the user is able to interactively select the typeof output displayed on their user interface. In some embodiments, thesensor output may have alternative configurations, such as is describedwith reference to FIG. 6, block 69, for example.

FIG. 5 is a block diagram of the receiver electronics in one embodiment.It may be noted that the receiver may comprise a configuration such asdescribed with reference to FIGS. 4A to 4D, above. Alternatively, thereceiver may comprise any configuration, including a desktop computer,laptop computer, a personal digital assistant (PDA), a server (local orremote to the receiver), or the like. In some embodiments, a receivermay be adapted to connect (via wired or wireless connection) to adesktop computer, laptop computer, a PDA, a server (local or remote tothe receiver), or the like in order to download data from the receiver.In some alternative embodiments, the receiver is housed within ordirectly connected to the sensor in a manner that allows sensor andreceiver electronics to work directly together and/or share dataprocessing resources. Accordingly, the receiver, including itselectronics, may be generally described as a “computer system.”

A quartz crystal 50 is operatively connected to an RF transceiver 51that together function to receive and synchronize data signals (e.g.,raw data signals transmitted from the RF transceiver). Once received,the microprocessor 52 processes the signals, such as described below.

The microprocessor 52 is the central control unit that provides thenecessary processing, such as calibration algorithms stored within anEEPROM 53. The EEPROM 53 is operatively connected to the microprocessor52 and provides semi-permanent storage of data, storing data such asreceiver ID and necessary programming to process data signals (e.g.,programming for performing calibration and other algorithms describedelsewhere herein). In some embodiments, an application-specificintegrated circuit (ASIC) may be used for some or all the receiver'scentral processing. An SRAM 54 is used for the system's cache memory andis helpful in data processing.

The microprocessor 52, which is operatively connected to EEPROM 53 andSRAM 54, controls the processing of the receiver electronics including,but not limited to, a sensor data receiving module, a reference datareceiving module, a data matching module, a calibration set module, aconversion function module, a sensor data transformation module, aquality evaluation module, a interface control module, and a stabilitydetermination module, which are described in more detail below. It maybe noted that any of the above processing may be programmed into andperformed in the sensor electronics (FIG. 2) in place of, or incomplement with, the receiver electronics (FIG. 5).

A battery 55 is operatively connected to the microprocessor 52 andprovides the necessary power for the receiver. In one embodiment, thebattery is a AAA battery, however any appropriately sized and poweredbattery may be used. In some embodiments, a plurality of batteries maybe used to power the system. A quartz crystal 56 is operativelyconnected to the microprocessor 52 and maintains system time for thecomputer system as a whole.

A user interface 57 comprises a keyboard, speaker, vibrator, backlight,LCD, and a plurality of buttons. The components that comprise the userinterface 57 provide the necessary controls to interact with the user. Akeyboard may allow, for example, input of user information abouthimself/herself, such as mealtime, exercise, insulin administration, andreference analyte values. A speaker may provide, for example, audiblesignals or alerts for conditions such as present and/or predicted hyper-and hypoglycemic conditions. A vibrator may provide, for example,tactile signals or alerts for reasons such as described with referenceto the speaker, above. A backlight may be provided, for example, to aidthe user in reading the LCD in low light conditions. An LCD may beprovided, for example, to provide the user with visual data output suchas described in more detail with reference to FIGS. 4A to 4D and FIG. 6.Buttons may provide toggle, menu selection, option selection, modeselection, and reset, for example.

Communication ports, including a personal computer (PC) corn port 58 anda reference analyte monitor corn port 59 may be provided to enablecommunication with systems that are separate from, or integral with, thereceiver. The PC corn port 58 comprises means for communicating withanother computer system (e.g., PC, PDA, server, or the like). In oneexemplary embodiment, the receiver is able to download historic data toa physician's PC for retrospective analysis by the physician. Thereference analyte monitor corn port 59 comprises means for communicatingwith a reference analyte monitor so that reference analyte values may beautomatically downloaded into the receiver. In one embodiment, thereference analyte monitor is integral with the receiver, and thereference analyte corn port 59 allows internal communication between thetwo integral systems. In another embodiment, the reference analytemonitor corn port 59 allows a wireless or wired connection to thereference analyte monitor such as a self-monitoring blood glucosemonitor (e.g., for measuring finger stick blood samples).

Algorithms

Reference is now made to FIG. 6, which is a flow chart that illustratesthe initial calibration and data output of the sensor data in oneembodiment.

Calibration of an analyte sensor comprises data processing that convertssensor data signal into an estimated analyte measurement that ismeaningful to a user. Accordingly, a reference analyte value is used tocalibrate the data signal from the analyte sensor.

At block 61, a sensor data receiving module, also referred to as thesensor data module, receives sensor data (e.g., a data stream),including one or more time-spaced sensor data points, from a sensor viathe receiver, which may be in wired or wireless communication with thesensor. The sensor data point(s) may be smoothed, such as described withreference to FIG. 3, above. It may be noted that during theinitialization of the sensor, prior to initial calibration, the receiver(e.g., computer system) receives and stores the sensor data, however maynot display any data to the user until initial calibration and possiblystabilization of the sensor has been determined.

At block 62, a reference data receiving module, also referred to as thereference input module, receives reference data from a reference analytemonitor, including one or more reference data points. In one embodiment,the reference analyte points may comprise results from a self-monitoredblood analyte test (e.g., from a finger stick test). In one suchembodiment, the user may administer a self-monitored blood analyte testto obtain an analyte value (e.g., point) using any known analyte sensor,and then enter the numeric analyte value into the computer system. Inanother such embodiment, a self-monitored blood analyte test comprises awired or wireless connection to the receiver (e.g. computer system) sothat the user simply initiates a connection between the two devices, andthe reference analyte data is passed or downloaded between theself-monitored blood analyte test and the receiver. In yet another suchembodiment, the self-monitored analyte test is integral with thereceiver so that the user simply provides a blood sample to thereceiver, and the receiver runs the analyte test to determine areference analyte value.

It may be noted that certain acceptability parameters may be set forreference values received from the user. For example, in one embodiment,the receiver may only accept reference analyte values between about 40and about 400 mg/dL. Other examples of determining valid referenceanalyte values are described in more detail with reference to FIG. 8.

At block 63, a data matching module, also referred to as the processormodule, matches reference data (e.g., one or more reference analyte datapoints) with substantially time corresponding sensor data (e.g., one ormore sensor data points) to provide one or more matched data pairs. Inone embodiment, one reference data point is matched to one timecorresponding sensor data point to form a matched data pair. In anotherembodiment, a plurality of reference data points are averaged (e.g.,equally or non-equally weighted average, mean-value, median, or thelike) and matched to one time corresponding sensor data point to form amatched data pair. In another embodiment, one reference data point ismatched to a plurality of time corresponding sensor data points averagedto form a matched data pair. In yet another embodiment, a plurality ofreference data points are averaged and matched to a plurality of timecorresponding sensor data points averaged to form a matched data pair.

In one embodiment, a time corresponding sensor data comprises one ormore sensor data points that occur 15±5 min after the reference analytedata timestamp (e.g., the time that the reference analyte data isobtained). In this embodiment, the 15 minute time delay has been chosento account for an approximately 10 minute delay introduced by the filterused in data smoothing and an approximately 5 minute physiologicaltime-lag (e.g., the time necessary for the analyte to diffusion througha membrane(s) of an analyte sensor). In alternative embodiments, thetime corresponding sensor value may be more or less than theabove-described embodiment, for example ±60 minutes. Variability in timecorrespondence of sensor and reference data may be attributed to, forexample a longer or shorter time delay introduced by the data smoothingfilter, or if the configuration of the analyte sensor incurs a greateror lesser physiological time lag.

It may be noted that in some practical implementations of the sensor,the reference analyte data may be obtained at a time that is differentfrom the time that the data is input into the receiver. Accordingly, itshould be noted that the “time stamp” of the reference analyte (e.g.,the time at which the reference analyte value was obtained) is not thesame as the time at which the reference analyte data was obtained byreceiver. Therefore, some embodiments include a time stamp requirementthat ensures that the receiver stores the accurate time stamp for eachreference analyte value, that is, the time at which the reference valuewas actually obtained from the user.

In some embodiments, tests are used to evaluate the best matched pairusing a reference data point against individual sensor values over apredetermined time period (e.g., about 30 minutes). In one suchexemplary embodiment, the reference data point is matched with sensordata points at 5-minute intervals and each matched pair is evaluated.The matched pair with the best correlation may be selected as thematched pair for data processing. In some alternative embodiments,matching a reference data point with an average of a plurality of sensordata points over a predetermined time period may be used to form amatched pair.

At block 64, a calibration set module, also referred to as the processormodule, forms an initial calibration set from a set of one or morematched data pairs, which are used to determine the relationship betweenthe reference analyte data and the sensor analyte data, such as will bedescribed in more detail with reference to block 67, below.

The matched data pairs, which make up the initial calibration set, maybe selected according to predetermined criteria. It may be noted thatthe criteria for the initial calibration set may be the same as, ordifferent from, the criteria for the update calibration set, which isdescribed in more detail with reference to FIG. 10. In some embodiments,the number (n) of data pair(s) selected for the initial calibration setis one. In other embodiments, n data pairs are selected for the initialcalibration set wherein n is a function of the frequency of the receivedreference data points. In one exemplary embodiment, six data pairs makeup the initial calibration set.

In some embodiments, the data pairs are selected only within a certainanalyte value threshold, for example wherein the reference analyte valueis between about 40 and about 400 mg/dL. In some embodiments, the datapairs that form the initial calibration set are selected according totheir time stamp. In some embodiments, the calibration set is selectedsuch as described with reference to FIG. 10

At block 65, a stability determination module, also referred to as thestart-up module, determines the stability of the analyte sensor over aperiod of time. It may be noted that some analyte sensors may have aninitial instability time period during which the analyte sensor isunstable for environmental, physiological, or other reasons. One exampleof initial sensor instability is an embodiment wherein the analytesensor is implanted subcutaneously; in this example embodiment,stabilization of the analyte sensor may be dependent upon the maturityof the tissue ingrowth around and within the sensor. Another example ofinitial sensor instability is in an embodiment wherein the analytesensor is implemented transdermally; in this example embodiment,stabilization of the analyte sensor may be dependent upon electrodestabilization and/or sweat, for example.

Accordingly, in some embodiments, determination of sensor stability mayinclude waiting a predetermined time period (e.g., an implantable sensoris known to require a time period for tissue, and a transdermal sensoris known to require time to equilibrate the sensor with the user'sskin); in some embodiments, this predetermined waiting period is betweenabout one minute and about six weeks. In some embodiments, thesensitivity (e.g., sensor signal strength with respect to analyteconcentration) may be used to determine the stability of the sensor; forexample, amplitude and/or variability of sensor sensitivity may beevaluated to determine the stability of the sensor. In alternativeembodiments, detection of pH levels, oxygen, hypochlorite, interferingspecies (e.g., ascorbate, urea, and acetaminophen), correlation betweensensor and reference values (e.g., R-value), baseline drift and/oroffset, and the like may be used to determine the stability of thesensor. In one exemplary embodiment, wherein the sensor is a glucosesensor, it is known to provide a signal that is associated withinterfering species (e.g., ascorbate, urea, acetaminophen), which may beused to evaluate sensor stability. In another exemplary embodiment,wherein the sensor is a glucose sensor such as described with referenceto FIGS. 1 and 2, the counter electrode can be monitored for oxygendeprivation, which may be used to evaluate sensor stability orfunctionality.

At decision block 66, the system (e.g., microprocessor) determineswhether the analyte sensor is sufficiently stable according to certaincriteria, such as described above. In one embodiment wherein the sensoris an implantable glucose sensor, the system waits a predetermined timeperiod believed necessary for sufficient tissue ingrowth and evaluatesthe sensor sensitivity (e.g., between about one minute and six weeks).In another embodiment, the receiver determines sufficient stabilitybased on oxygen concentration near the sensor head. In yet anotherembodiment, the sensor determines sufficient stability based on areassessment of baseline drift and/or offset. In yet another alternativeembodiment, the system evaluates stability by monitoring the frequencycontent of the sensor data stream over a predetermined amount of time(e.g., 24 hours); in this alternative embodiment, a template (ortemplates) are provided that reflect acceptable levels of glucosephysiology and are compared with the actual sensor data, wherein apredetermined amount of agreement between the template and the actualsensor data is indicative of sensor stability. It may be noted that afew examples of determining sufficient stability are given here, howevera variety of known tests and parameters may be used to determine sensorstability without departing from the spirit and scope of the preferredembodiments.

If the receiver does not assess that the stability of the sensor issufficient, then the processing returns to block 61, wherein thereceiver receives sensor data such as described in more detail above.The above-described steps are repeated until sufficient stability isdetermined.

If the receiver does assess that the stability of the sensor issufficient, then processing continues to block 67 and the calibrationset is used to calibrate the sensor.

At block 67, the conversion function module uses the calibration set tocreate a conversion function. The conversion function substantiallydefines the relationship between the reference analyte data and theanalyte sensor data.

A variety of known methods may be used with the preferred embodiments tocreate the conversion function from the calibration set. In oneembodiment, wherein a plurality of matched data points form the initialcalibration set, a linear least squares regression is performed on theinitial calibration set such as described with reference to FIG. 7.

FIG. 7 is a graph that illustrates a regression performed on acalibration set to create a conversion function in one exemplaryembodiment. In this embodiment, a linear least squares regression isperformed on the initial calibration set. The x-axis representsreference analyte data; the y-axis represents sensor data. The graphpictorially illustrates regression of the matched pairs 76 in thecalibration set. Regression calculates a slope 72 and an offset 74(y=m×+b), which defines the conversion function.

In alternative embodiments other algorithms could be used to determinethe conversion function, for example forms of linear and non-linearregression, for example fuzzy logic, neural networks, piece-wise linearregression, polynomial fit, genetic algorithms, and other patternrecognition and signal estimation techniques.

In yet other alternative embodiments, the conversion function maycomprise two or more different optimal conversions because an optimalconversion at any time is dependent on one or more parameters, such astime of day, calories consumed, exercise, or analyte concentration aboveor below a set threshold, for example. In one such exemplary embodiment,the conversion function is adapted for the estimated glucoseconcentration (e.g., high vs. low). For example in an implantableglucose sensor it has been observed that the cells surrounding theimplant will consume at least a small amount of glucose as it diffusestoward the glucose sensor. Assuming the cells consume substantially thesame amount of glucose whether the glucose concentration is low or high,this phenomenon will have a greater effect on the concentration ofglucose during low blood sugar episodes than the effect on theconcentration of glucose during relatively higher blood sugar episodes.Accordingly, the conversion function is adapted to compensate for thesensitivity differences in blood sugar level. In one implementation, theconversion function comprises two different regression lines wherein afirst regression line is applied when the estimated blood glucoseconcentration is at or below a certain threshold (e.g., 150 mg/dL) and asecond regression line is applied when the estimated blood glucoseconcentration is at or above a certain threshold (e.g., 150 mg/dL). Inone alternative implementation, a predetermined pivot of the regressionline that forms the conversion function may be applied when theestimated blood is above or below a set threshold (e.g., 150 mg/dL),wherein the pivot and threshold are determined from a retrospectiveanalysis of the performance of a conversion function and its performanceat a range of glucose concentrations. In another implementation, theregression line that forms the conversion function is pivoted about apoint in order to comply with clinical acceptability standards (e.g.,Clarke Error Grid, Consensus Grid, mean absolute relative difference, orother clinical cost function). Although only a few exampleimplementations are described, the preferred embodiments contemplatenumerous implementations wherein the conversion function is adaptivelyapplied based on one or more parameters that may affect the sensitivityof the sensor data over time.

Referring again to FIG. 6, at block 68, a sensor data transformationmodule uses the conversion function to transform sensor data intosubstantially real-time analyte value estimates, also referred to ascalibrated data, as sensor data is continuously (or intermittently)received from the sensor. For example, in the embodiment of FIG. 7, thesensor data, which may be provided to the receiver in “counts”, istranslated in to estimate analyte value(s) in mg/dL. In other words, theoffset value at any given point in time may be subtracted from the rawvalue (e.g., in counts) and divided by the slope to obtain the estimateanalyte value:

${{mg}\text{/}{dL}} = \frac{\left( {{rawvalue} - {offset}} \right)}{slope}$

In some alternative embodiments, the sensor and/or reference analytevalues are stored in a database for retrospective analysis.

At block 69, an output module provides output to the user via the userinterface. The output is representative of the estimated analyte value,which is determined by converting the sensor data into a meaningfulanalyte value such as described in more detail with reference to block68, above. User output may be in the form of a numeric estimated analytevalue, an indication of directional trend of analyte concentration,and/or a graphical representation of the estimated analyte data over aperiod of time, for example. Other representations of the estimatedanalyte values are also possible, for example audio and tactile.

In one exemplary embodiment, such as shown in FIG. 4A, the estimatedanalyte value is represented by a numeric value. In other exemplaryembodiments, such as shown in FIGS. 4B to 4D, the user interfacegraphically represents the estimated analyte data trend overpredetermined a time period (e.g., one, three, and nine hours,respectively). In alternative embodiments, other time periods may berepresented.

In some embodiments, the user interface begins displaying data to theuser after the sensor's stability has been affirmed. In some alternativeembodiments however, the user interface displays data that is somewhatunstable (e.g., does not have sufficient stability at block 66); inthese embodiments, the receiver may also include an indication ofinstability of the sensor data (e.g., flashing, faded, or anotherindication of sensor instability displayed on the user interface). Insome embodiments, the user interface informs the user of the status ofthe stability of the sensor data.

Accordingly, after initial calibration of the sensor, and possiblydetermination of stability of the sensor data, real-time continuousanalyte information may be displayed on the user interface so that theuser may regularly and proactively care for his/her diabetic conditionwithin the bounds set by his/her physician.

In alternative embodiments, the conversion function is used to predictanalyte values at future points in time. These predicted values may beused to alert the user of upcoming hypoglycemic or hyperglycemic events.Additionally, predicted values may be used to compensate for the timelag (e.g., 15 minute time lag such as described elsewhere herein), sothat an estimate analyte value displayed to the user represents theinstant time, rather than a time delayed estimated value.

In some embodiments, the substantially real time estimated analytevalue, a predicted future estimate analyte value, a rate of change,and/or a directional trend of the analyte concentration is used tocontrol the administration of a constituent to the user, including anappropriate amount and time, in order to control an aspect of the user'sbiological system. One such example is a closed loop glucose sensor andinsulin pump, wherein the analyte data (e.g., estimated glucose value,rate of change, and/or directional trend) from the glucose sensor isused to determine the amount of insulin, and time of administration,that may be given to a diabetic user to evade hyper- and hypoglycemicconditions.

Reference is now made to FIG. 8, which is a flow chart that illustratesthe process of evaluating the clinical acceptability of reference andsensor data in one embodiment. Although some clinical acceptabilitytests are disclosed here, any known clinical standards and methodologiesmay be applied to evaluate the clinical acceptability of reference andanalyte data herein.

It may be noted that the conventional analyte meters (e.g.,self-monitored blood analyte tests) are known to have a +−20% error inanalyte values. For example, gross errors in analyte readings are knownto occur due to patient error in self-administration of the bloodanalyte test. In one such example, if the user has traces of sugar onhis/her finger while obtaining a blood sample for a glucoseconcentration test, then the measured glucose value will likely be muchhigher than the actual glucose value in the blood. Additionally, it isknown that self-monitored analyte tests (e.g., test strips) areoccasionally subject to manufacturing error.

Another cause for error includes infrequency and time delay that mayoccur if a user does not self-test regularly, or if a user self-testsregularly but does not enter the reference value at the appropriate timeor with the appropriate time stamp. Therefore, it may be advantageous tovalidate the acceptability of reference analyte values prior toaccepting them as valid entries. Accordingly, the receiver evaluates theclinical acceptability of received reference analyte data prior to theiracceptance as a valid reference value.

In one embodiment, the reference analyte data (and/or sensor analytedata) is evaluated with respect to substantially time correspondingsensor data (and/or substantially time corresponding reference analytedata) to determine the clinical acceptability of the reference analyteand/or sensor analyte data. Clinical acceptability considers a deviationbetween time corresponding glucose measurements (e.g., data from aglucose sensor and data from a reference glucose monitor) and the risk(e.g., to the decision making of a diabetic patient) associated withthat deviation based on the glucose value indicated by the sensor and/orreference data. Evaluating the clinical acceptability of reference andsensor analyte data, and controlling the user interface dependentthereon, may minimize clinical risk.

In one embodiment, the receiver evaluates clinical acceptability eachtime reference data is obtained. In another embodiment, the receiverevaluates clinical acceptability after the initial calibration andstabilization of the sensor, such as described with reference to FIG. 6,above. In some embodiments, the receiver evaluates clinicalacceptability as an initial pre-screen of reference analyte data, forexample after determining if the reference glucose measurement isbetween about 40 and 400 mg/dL. In other embodiments, other methods ofpre-screening data may be used, for example by determining if areference analyte data value is physiologically feasible based onprevious reference analyte data values (e.g., below a maximum rate ofchange).

After initial calibration such as described in more detail withreference to FIG. 6, the sensor data receiving module 61 receivessubstantially continuous sensor data (e.g., a data stream) via areceiver and converts that data into estimated analyte values. As usedherein, “substantially continuous” is broad enough to include a datastream of individual measurements taken at time intervals (e.g.,time-spaced) ranging from fractions of a second up to, e.g., 1, 2, or 5minutes. As sensor data is continuously converted, it may beoccasionally recalibrated such as described in more detail withreference FIG. 10. Initial calibration and re-calibration of the sensorrequires a reference analyte value. Accordingly, the receiver mayreceive reference analyte data at any time for appropriate processing.These reference analyte values may be evaluated for clinicalacceptability such as described below as a fail-safe against referenceanalyte test errors.

At block 81, the reference data receiving module, also referred to asthe reference input module, receives reference analyte data from areference analyte monitor. In one embodiment, the reference datacomprises one analyte value obtained from a reference monitor. In somealternative embodiments however, the reference data includes a set ofanalyte values entered by a user into the interface and averaged byknown methods such as described elsewhere herein.

In some embodiments, the reference data is pre-screened according toenvironmental and physiological issues, such as time of day, oxygenconcentration, postural effects, and patient-entered environmental data.In one example embodiment, wherein the sensor comprises an implantableglucose sensor, an oxygen sensor within the glucose sensor is used todetermine if sufficient oxygen is being provided to successfullycomplete the necessary enzyme and electrochemical reactions for glucosesensing. In another example embodiment wherein the sensor comprises animplantable glucose sensor, the counter electrode could be monitored fora “rail-effect”, that is, when insufficient oxygen is provided at thecounter electrode causing the counter electrode to reach operational(e.g., circuitry) limits. In yet another example embodiment, the patientis prompted to enter data into the user interface, such as meal timesand/or amount of exercise, which could be used to determine likelihoodof acceptable reference data.

It may be further noted that evaluation data, such as described in theparagraph above, may be used to evaluate an optimum time for referenceanalyte measurement. Correspondingly, the user interface may then promptthe user to provide a reference data point for calibration within agiven time period. Consequently, because the receiver proactivelyprompts the user during optimum calibration times, the likelihood oferror due to environmental and physiological limitations may decreaseand consistency and acceptability of the calibration may increase.

At block 82, the clinical acceptability evaluation module, also referredto as clinical module, evaluates the clinical acceptability of newlyreceived reference data and/or time corresponding sensor data. In someembodiments of evaluating clinical acceptability, the rate of change ofthe reference data as compared to previous data is assessed for clinicalacceptability. That is, the rate of change and acceleration (ordeceleration) of many analytes has certain physiological limits withinthe body. Accordingly, a limit may be set to determine if the newmatched pair is within a physiologically feasible range, indicated by arate of change from the previous data that is within known physiologicaland/or statistical limits. Similarly, in some embodiments any algorithmthat predicts a future value of an analyte may be used to predict andthen compare an actual value to a time corresponding predicted value todetermine if the actual value falls within a clinically acceptable rangebased on the predictive algorithm, for example.

In one exemplary embodiment, the clinical acceptability evaluationmodule 82 matches the reference data with a substantially timecorresponding converted sensor value such as described with reference toFIG. 6 above, and plots the matched data on a Clarke Error Grid such asdescribed in more detail with reference to FIG. 9.

FIG. 9 is a graph of two data pairs on a Clarke Error Grid to illustratethe evaluation of clinical acceptability in one exemplary embodiment.The Clarke Error Grid may be used by the clinical acceptabilityevaluation module to evaluate the clinical acceptability of thedisparity between a reference glucose value and a sensor glucose (e.g.,estimated glucose) value, if any, in an embodiment wherein the sensor isa glucose sensor. The x-axis represents glucose reference glucose dataand the y-axis represents estimated glucose sensor data. Matched datapairs are plotted accordingly to their reference and sensor values,respectively. In this embodiment, matched pairs that fall within the Aand B regions of the Clarke Error Grid are considered clinicallyacceptable, while matched pairs that fall within the C, D, and E regionsof the Clarke Error Grid are not considered clinically acceptable.Particularly, FIG. 9 shows a first matched pair 92 is shown which fallswithin the A region of the Clarke Error Grid, therefore is it consideredclinically acceptable. A second matched pair 94 is shown which fallswithin the C region of the Clarke Error Grid, therefore it is notconsidered clinically acceptable.

It may be noted that a variety of other known methods of evaluation ofclinical acceptability may be utilized. In one alternative embodiment,the Consensus Grid is used to evaluate the clinical acceptability ofreference and sensor data. In another alternative embodiment, a meanabsolute difference calculation may be used to evaluate the clinicalacceptability of the reference data. In another alternative embodiment,the clinical acceptability may be evaluated using any relevant clinicalacceptability test, such as a known grid (e.g., Clarke Error orConsensus), and including additional parameters such as time of dayand/or the increase or decreasing trend of the analyte concentration. Inanother alternative embodiment, a rate of change calculation may be usedto evaluate clinical acceptability. In yet another alternativeembodiment, wherein the received reference data is in substantially realtime, the conversion function could be used to predict an estimatedglucose value at a time corresponding to the time stamp of the referenceanalyte value (this may be required due to a time lag of the sensor datasuch as described elsewhere herein). Accordingly, a threshold may be setfor the predicted estimated glucose value and the reference analytevalue disparity, if any.

Referring again to FIG. 8, the results of the clinical acceptabilityevaluation are assessed. If clinical acceptability is determined withthe received reference data, then processing continues to block 84 tooptionally recalculate the conversion function using the receivedreference data in the calibration set. If, however, clinicalacceptability is not determined, then the processing progresses to block86 to control the user interface, such as will be described withreference to block 86 below.

At block 84, the conversion function module optionally recreates theconversion function using the received reference data. In oneembodiment, the conversion function module adds the newly receivedreference data (e.g., including the matched sensor data) into thecalibration set, displaces the oldest, and/or least concordant matcheddata pair from the calibration set, and recalculates the conversionfunction accordingly. In another embodiment, the conversion functionmodule evaluates the calibration set for best calibration based oninclusion criteria, such as described in more detail with reference toFIG. 10.

At 85, the sensor data transformation module uses the conversionfunction to continually (or intermittently) convert sensor data intoestimated analyte values, also referred to as calibrated data, such asdescribed in more detail with reference to FIG. 6, block 68.

At block 86, the interface control module, also referred to as thefail-safe module, controls the user interface based upon the clinicalacceptability of the reference data received. If the evaluation (block82) deems clinical acceptability, then the user interface may functionas normal; that is, providing output for the user such as described inmore detail with reference to FIG. 6, block 69.

If however the reference data is not considered clinically acceptable,then the fail-safe module begins the initial stages of fail-safe mode.In some embodiments, the initial stages of fail-safe mode includealtering the user interface so that estimated sensor data is notdisplayed to the user. In some embodiments, the initial stages offail-safe mode include prompting the user to repeat the referenceanalyte test and provide another reference analyte value. The repeatedanalyte value is then evaluated for clinical acceptability such asdescribed with reference to blocks 81 to 83, above.

If the results of the repeated analyte test are determined to beclinically unacceptable, then fail-safe module may alter the userinterface to reflect full fail-safe mode. In one embodiment, fullfail-safe mode includes discontinuing sensor analyte display output onthe user interface. In other embodiments, color-coded information, trendinformation, directional information (e.g., arrows or angled lines),gauges, and/or fail-safe information may be displayed, for example.

If the results of the repeated analyte test are determined to beclinically acceptable, then the first analyte value is discarded, andthe repeated analyte value is accepted. The process returns to block 84to optionally recalculate the conversion function, such as described inmore detail with reference to block 84, above.

Reference is now made to FIG. 10, which is a flow chart that illustratesthe process of evaluation of calibration data for best calibration basedon inclusion criteria of matched data pairs in one embodiment.

It may be noted that calibration of analyte sensors may be variable overtime; that is, the conversion function suitable for one point in timemay not be suitable for another point in time (e.g., hours, days, weeks,or months later). For example, in an embodiment wherein the analytesensor is subcutaneously implantable, the maturation of tissue ingrowthover time may cause variability in the calibration of the analytesensor. As another example, physiological changes in the user (e.g.,metabolism, interfering blood constituents, lifestyle changes) may causevariability in the calibration of the sensor. Accordingly, acontinuously updating calibration algorithm is disclosed that includesreforming the calibration set, and thus recalculating the conversionfunction, over time according to a set of inclusion criteria.

At block 101, the reference data receiving module, also referred to asthe reference input module, receives a new reference analyte value(e.g., data point) from the reference analyte monitor. In someembodiments, the reference analyte value may be pre-screened accordingto criteria such as described in more detail with reference to FIG. 6,block 62. In some embodiments, the reference analyte value may beevaluated for clinical acceptability such as described in more detailwith reference to FIG. 8.

At block 102, the data matching module, also referred to as theprocessor module, forms one or more updated matched data pairs bymatching new reference data to substantially time corresponding sensordata, such as described in more detail with reference to FIG. 6, block63.

At block 103, a calibration evaluation module evaluates the new matchedpair(s) inclusion into the calibration set. In some embodiments, thereceiver simply adds the updated matched data pair into the calibrationset, displaces the oldest and/or least concordant matched pair from thecalibration set, and proceeds to recalculate the conversion functionaccordingly (block 105).

In some embodiments, the calibration evaluation includes evaluating onlythe new matched data pair. In some embodiments, the calibrationevaluation includes evaluating all of the matched data pairs in theexisting calibration set and including the new matched data pair; insuch embodiments not only is the new matched data pair evaluated forinclusion (or exclusion), but additionally each of the data pairs in thecalibration set are individually evaluated for inclusion (or exclusion).In some alternative embodiments, the calibration evaluation includesevaluating all possible combinations of matched data pairs from theexisting calibration set and including the new matched data pair todetermine which combination best meets the inclusion criteria. In someadditional alternative embodiments, the calibration evaluation includesa combination of at least two of the above-described embodiments.

Inclusion criteria comprise one or more criteria that define a set ofmatched data pairs that form a substantially optimal calibration set.One inclusion criterion comprises ensuring the time stamp of the matcheddata pairs (that make up the calibration set) span at least a set timeperiod (e.g., three hours). Another inclusion criterion comprisesensuring that the time stamps of the matched data pairs are not morethan a set age (e.g., one week old). Another inclusion criterion ensuresthat the matched pairs of the calibration set have a substantiallydistributed amount of high and low raw sensor data, estimated sensoranalyte values, and/or reference analyte values. Another criterioncomprises ensuring all raw sensor data, estimated sensor analyte values,and/or reference analyte values are within a predetermined range (e.g.,40 to 400 mg/dL for glucose values). Another criterion comprisesevaluating the rate of change of the analyte concentration (e.g., fromsensor data) during the time stamp of the matched pair(s). For example,sensor and reference data obtained during the time when the analyteconcentration is undergoing a slow rate of change may be lesssusceptible inaccuracies caused by time lag and other physiological andnon-physiological effects. Another criterion comprises evaluating thecongruence of respective sensor and reference data in each matched datapair; the matched pairs with the most congruence may be chosen. Anothercriterion comprises evaluating physiological changes (e.g., low oxygendue to a user's posture that may effect the function of a subcutaneouslyimplantable analyte sensor, or other effects such as described withreference to FIG. 6) to ascertain a likelihood of error in the sensorvalue. It may be noted that evaluation of calibration set criteria maycomprise evaluating one, some, or all of the above described inclusioncriteria. It is contemplated that additional embodiments may compriseadditional inclusion criteria not explicitly described herein.

At block 104, the evaluation of the calibration set determines whetherto maintain the previously established calibration set, or if thecalibration set should be updated (e.g., modified) with the new matcheddata pair. In some embodiments, the oldest matched data pair is simplydisplaced when a new matched data pair is included. It may be notedhowever that a new calibration set may include not only thedetermination to include the new matched data pair, but in someembodiments, may also determine which of the previously matched datapairs should be displaced from the calibration set.

At block 105, the conversion function module recreates the conversionfunction using the modified calibration set. The calculation of theconversion function is described in more detail with reference to FIG.6.

At block 106, the sensor data transformation module converts sensor datato calibrated data using the updated conversion function. Conversion ofraw sensor data into estimated analyte values is described in moredetail with reference to FIG. 6.

Reference is now made to FIG. 11, which is a flow chart that illustratesthe process of evaluating the quality of the calibration in oneembodiment. The calibration quality may be evaluated by determining thestatistical association of data that forms the calibration set, whichdetermines the confidence associated with the conversion function usedin calibration and conversion of raw sensor data into estimated analytevalues.

In one embodiment calibration quality may be evaluated after initial orupdated calculation of the conversion function such as describedelsewhere herein. However it may be noted that calibration quality maybe performed at any time during the data processing.

At block 111, a sensor data receiving module, also referred to as thesensor data module, receives the sensor data from the sensor such asdescribed in more detail with reference to FIG. 6.

At block 112, a reference data receiving module, also referred to as thereference input module, receives reference data from a reference analytemonitor, such as described in more detail with reference to FIG. 6.

At block 113, the data matching module, also referred to as theprocessor module, matches received reference data with substantiallytime corresponding sensor data to provide one or more matched datapairs, such as described in more detail with reference to FIG. 6.

At block 114, the calibration set module, also referred to as theprocessor module, forms a calibration set from one or more matched datapairs such as described in more detail with reference to FIGS. 6, 8, and10.

At block 115, the conversion function module calculates a conversionfunction using the calibration set, such as described in more detailwith reference to FIGS. 6, 8, and 10.

At block 116, the sensor data transformation module continuously (orintermittently) converts received sensor data into estimated analytevalues, also referred to as calibrated data, such as described in moredetail with reference to FIGS. 6, 8, and 10.

At block 117, a quality evaluation module evaluates the quality of thecalibration. In one embodiment, the quality of the calibration is basedon the association of the calibration set data using statisticalanalysis. Statistical analysis may comprise any known cost function suchas linear regression, non-linear mapping/regression, rank (e.g.,non-parametric) correlation, least mean square fit, mean absolutedeviation (MAD), mean absolute relative difference, and the like. Theresult of the statistical analysis provides a measure of the associationof data used in calibrating the system. A threshold of data associationmay be set to determine if sufficient quality is exhibited in acalibration set.

In another embodiment, the quality of the calibration is determined byevaluating the calibration set for clinical acceptability, such asdescribed with reference to blocks 82 and 83 (e.g., Clarke Error Grid,Consensus Grid, or clinical acceptability test). As an example, thematched data pairs that form the calibration set may be plotted on aClarke Error Grid, such that when all matched data pairs fall within theA and B regions of the Clarke Error Grid, then the calibration isdetermined to be clinically acceptable.

In yet another alternative embodiment, the quality of the calibration isdetermined based initially on the association of the calibration setdata using statistical analysis, and then by evaluating the calibrationset for clinical acceptability. If the calibration set fails thestatistical and/or the clinical test, the processing returns to block115 to recalculate the conversion function with a new (e.g., optimized)set of matched data pairs. In this embodiment, the processing loop(block 115 to block 117) iterates until the quality evaluation module 1)determines clinical acceptability, 2) determines sufficient statisticaldata association, 3) determines both clinical acceptability andsufficient statistical data association, or 4) surpasses a threshold ofiterations; after which the processing continues to block 118.

FIGS. 12A and 12B illustrate one exemplary embodiment wherein theaccuracy of the conversion function is determined by evaluating thecorrelation coefficient from linear regression of the calibration setthat formed the conversion function. In this exemplary embodiment, athreshold (e.g., 0.79) is set for the R-value obtained from thecorrelation coefficient.

FIGS. 12A and 12B are graphs that illustrate an evaluation of thequality of calibration based on data association in one exemplaryembodiment using a correlation coefficient. Particularly, FIGS. 12A and12B pictorially illustrate the results of the linear least squaresregression performed on a first and a second calibration set (FIGS. 12Aand 12B, respectively). The x-axis represents reference analyte data;the y-axis represents sensor data. The graph pictorially illustratesregression that determines the conversion function.

It may be noted that the regression line (and thus the conversionfunction) formed by the regression of the first calibration set of FIG.12A is the same as the regression line (and thus the conversionfunction) formed by the regression of the second calibration set of FIG.12B. However, the correlation of the data in the calibration set to theregression line in FIG. 12A is significantly different than thecorrelation of the data in the calibration set to the regression line inFIG. 12A. In other words, there is a noticeably greater deviation of thedata from the regression line in FIG. 12B than the deviation of the datafrom the regression line in FIG. 12A.

In order to quantify this difference in correlation, an R-value may beused to summarize the residuals (e.g., root mean square deviations) ofthe data when fitted to a straight line via least squares method, inthis exemplary embodiment. R-value may be calculated according to thefollowing equation:

$R = \frac{\sum\limits_{i}{\left( {x_{i} - \overset{\_}{x}} \right)\left( {y_{i} - \overset{\_}{y}} \right)}}{\sqrt{\sum\limits_{i}\left( {x_{i} - x} \right)^{2}}\sqrt{\left. {{\sum\limits_{i}y_{i}} - y} \right)^{2}}}$In the above equation: i is an index (1 to n), x is a reference analytevalue, y is a sensor analyte value, x is an average of 1/n referenceanalyte values, and y is an average of 1/n sensor analyte values.

In the exemplary calibration set shown in FIG. 12A, the calculatedR-value is about 0.99, which may also be expressed as the correlationcoefficient of regression. Accordingly, the calibration exhibitssufficient data association (and thus insufficient quality) because itfalls above the 0.79 threshold set in this exemplary embodiment.

In the exemplary calibration set shown in FIG. 12B, the calculatedR-value is about 0.77, which may also be expressed as the correlationcoefficient of regression. Accordingly, the calibration exhibitsinsufficient data association (and thus insufficient quality) because itfalls below the 0.79 threshold set in this exemplary embodiment.

Reference is again made to FIG. 11, at block 118, the interface controlmodule, also referred to as the fail-safe module, controls the userinterface based upon the quality of the calibration. If the calibrationis exhibits sufficient quality, then the user interface may function asnormal; that is providing output for the user such as described in moredetail with reference to FIG. 6.

If however the calibration is not deemed sufficient in quality, thenfail-safe module 118 begins the initial stages of fail-safe mode, whichare described in more detail with reference to FIG. 8. In someembodiments, the initial stages of fail-safe mode include altering theuser interface so that estimated sensor data is not displayed to theuser. In some embodiments, the initial stages of fail-safe mode alsoinclude prompting the user to provide an updated reference analytevalue. The updated analyte value is then processed as described aboveand the updated conversion function that results from the repeatedreference analyte test, if any, is evaluated for statistical accuracy.

If the results of the updated evaluation again exhibit insufficientquality, then the fail-safe module alters user interface to reflect fullfail-safe mode, which is described in more detail with reference to FIG.8. If however the results of the updated evaluation exhibit sufficientquality, then the first reference analyte value is discarded, and therepeated reference analyte value is accepted and the process continuesas described herein.

It may be noted that the initial stages of fail-safe mode and full failsafe mode may be similar to that described with reference to FIG. 8,including user interface control for example. Additionally, it iscontemplated herein that a variety of difference modes between initialand full fail-safe mode may be provided depending on the relativequality of the calibration. In other words, the confidence level of thecalibration quality may control a plurality of different user interfacescreens providing error bars, ±values, and the like. Similar screens maybe implements in the clinical acceptability embodiments described withreference to FIG. 8.

The above description discloses several methods and materials of thedisclosed invention. This invention is susceptible to modifications inthe methods and materials, as well as alterations in the fabricationmethods and equipment. Such modifications will become apparent to thoseskilled in the art from a consideration of this disclosure or practiceof the invention disclosed herein. Consequently, it is not intended thatthis invention be limited to the specific embodiments disclosed herein,but that it cover all modifications and alternatives coming within thetrue scope and spirit of the invention as embodied in the attachedclaims. All patents, applications, and other references cited herein arehereby incorporated by reference in their entirety.

1. A method for calibrating a substantially continuous analyte sensor, the method comprising: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, comprising one or more reference data points; providing at least one matched data pair by matching reference analyte data to substantially time corresponding sensor data; creating a conversion function based on said at least one matched data pair; evaluating said at least one matched data pair, including at least one of 1) ensuring said at least one matched data pair is within a predetermined time range, 2) ensuring said at least one matched data pair is no older than a predetermined value, 3) ensuring said at least one matched data pair is substantially distributed with additional matched data pairs, if present, between high and low matched data pairs over a predetermined time range, and 4) ensuring said at least one matched data pair is within a predetermined range of analyte values, wherein the step of evaluating said at least one matched data pair further comprises at least one of evaluating a rate of change of the analyte concentration, evaluating a congruence of respective sensor and reference data in a matched data pair, and evaluating physiological changes; and subsequently modifying said conversion function if such modification is required by said evaluation.
 2. The method of claim 1, wherein the step of evaluating comprises evaluating an initial matched data pair.
 3. The method of claim 1, wherein the step of evaluating comprises evaluating at least one subsequently received matched data pair.
 4. The method of claim 1, wherein the step of evaluating comprises evaluating a plurality of matched data pairs.
 5. The method of claim 1, wherein the step of receiving sensor data comprises receiving a data stream from an implantable analyte sensor.
 6. The method of claim 1, wherein the step of receiving sensor data comprises receiving a data stream that has been algorithmically smoothed.
 7. The method of claim 1, wherein the step of receiving sensor data stream comprises algorithmically smoothing said data stream.
 8. The method of claim 1, wherein the step of receiving reference data comprises downloading reference data via a wireless connection.
 9. The method of claim 1, wherein the step of receiving reference data comprises downloading reference data via a wireless connection.
 10. The method of claim 1, wherein the step of receiving reference data from a reference analyte monitor comprises receiving within a receiver internal communication from a reference analyte monitor integral with said receiver.
 11. The method of claim 1, wherein the reference analyte monitor comprises self-monitoring of blood analyte.
 12. The method of claim 1, wherein the step of creating a conversion function comprises linear regression.
 13. The method of claim 1, wherein the step of creating a conversion function comprises non-linear regression.
 14. The method of claim 1, wherein the step of creating a conversion function is based on between one matched data pair and six matched data pairs.
 15. The method of claim 1, wherein the step of creating a conversion function is based on at least two matched data pairs.
 16. A method for calibrating a substantially continuous analyte sensor, the method comprising: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, comprising one or more reference data points; providing at least one matched data pair by matching reference analyte data to substantially time corresponding sensor data; forming a calibration set including said at least one matching data pair; creating a conversion function based on said calibration set; converting sensor data into calibrated data using said conversion function; subsequently obtaining one or more additional reference data points and creating one or more new matched data pair; evaluating said calibration set when said new matched data pair is created, wherein evaluating said calibration set includes at least one of 1) ensuring matched data pair in said calibration set span a predetermined time range, 2) ensuring matched data pair in said calibration set are no older than a predetermined value, 3) ensuring said calibration set has substantially distributed high and low matched data pair over said predetermined time range, and 4) allowing matched data pair only within a predetermined range of analyte values; and subsequently modifying said calibration set if such modification is required by said evaluation, wherein the step of forming a calibration set further comprises determining a value for n, where n is greater than one and represents the number of matched data pair in the calibration set, wherein the step of determining a value for n is determined as a function of the frequency of the received reference data points and signal strength over time.
 17. The method of claim 1, further comprising determining a set of matched data pairs responsive to the evaluating step.
 18. The method of claim 17, further comprising repeating the step of creating said conversion function using said set of matched data pairs.
 19. The method of claim 1 or 18, further comprising converting sensor data into calibrated data using said conversion function.
 20. A system for calibrating a substantially continuous analyte sensor, the system comprising: means for receiving a data stream from an analyte sensor, a plurality of time-spaced sensor data points; means for receiving reference data from a reference analyte monitor, comprising one or more reference data points; means for providing one or more matched data pairs by matching reference analyte data to substantially time corresponding sensor data; means for creating a conversion function based on said one or more matched data pairs; means for evaluating said one or more matched data pairs including at least one of 1) ensuring one or more matched data pairs is within a predetermined time range, 2) ensuring one or more matched data pairs are no older than a predetermined value, 3) ensuring said one or more matched data pairs have substantially distributed high and low matched data pairs over a predetermined time range, and 4) ensuring said one or more matched data pairs are within a predetermined range of analyte values, wherein said means for evaluating said one or more matched data pairs further comprises at least one means for evaluating a rate of change of the analyte concentration, means for evaluating a congruence of respective sensor and reference data in matched data pairs, and means for evaluating physiological changes; and means for modifying said conversion function if such modification is required by said means for evaluating.
 21. The system of claim 20, wherein said means for evaluating comprises means for evaluating one or more initial matched data pairs.
 22. The system of claim 20, wherein said means for evaluating comprises means for evaluating one or more subsequently received matched data pairs.
 23. The system of claim 20, wherein said means for evaluating comprises means for evaluating a plurality of matched data pairs.
 24. The system of claim 20, wherein said means for receiving sensor data comprises means for receiving sensor data from an implantable analyte sensor.
 25. The system of claim 20, wherein said means for receiving sensor data comprises means for receiving sensor data that has been algorithmically smoothed.
 26. The system of claim 20, wherein said means for receiving sensor data comprises means for algorithmically smoothing said receiving sensor data.
 27. The system of claim 20, wherein said means for receiving reference data comprises means for downloading reference data via a cabled connection.
 28. The system of claim 20, wherein said means for receiving reference data comprises means for downloading reference data via a wireless connection.
 29. The system of claim 20, wherein said means for receiving reference data from a reference analyte monitor comprises means for receiving within a receiver internal communication from a reference analyte monitor integral with said receiver.
 30. The system of claim 20, wherein said means for receiving reference data comprises means for receiving from a self monitoring of blood analyte.
 31. The system of claim 20, wherein said means for creating a conversion function comprises means for performing linear regression.
 32. The system of claim 20, wherein said means for creating a conversion function comprises means for performing non-linear regression.
 33. The system of claim 20, wherein said means for creating a conversion function is based on between one matched data pair and six matched data pairs.
 34. The system of claim 23, wherein said means for creating a conversion function is based on at least two matched data pairs.
 35. A system for calibrating a substantially continuous analyte sensor, the system comprising: means for receiving a data stream from an analyte sensor, a plurality of time-spaced sensor data points; means for receiving reference data from a reference analyte monitor, comprising one or more reference data points; means for providing one or more matched data pair by matching reference analyte data to substantially time corresponding sensor data; means for forming a calibration set comprising at least one matched data pair; means for creating a conversion function based on said calibration set; means for converting sensor data into calibrated data using said conversion function; subsequently obtaining one or more additional reference data points and creating one or more new matched data pair; means for evaluating said calibration set when said new matched data pair is created, wherein evaluating said calibration set includes at least one of 1) ensuring matched data pair in said calibration set span a predetermined time range, 2) ensuring matched data pair in said calibration set are no older than a predetermined value, 3) ensuring said calibration set has substantially distributed high and low matched data pair over said predetermined time range, and 4) allowing matched data pair only within a predetermined range of analyte values; and means for modifying said calibration set if such modification is required by said evaluation wherein the means for forming a calibration set further comprises determining a value for n, where n is greater than one and represents the number of matched data pair in the calibration set, and wherein the means for determining a value for n is determined as a function of the frequency of the received reference data points and signal strength over time.
 36. The system of claim 20, further comprising means for determining a set of matched data pairs from said evaluation.
 37. The system of claim 36, further comprising said means for repeating the step of creating a conversion function using said set of matched data pairs.
 38. The system of claim 37, further comprising means for converting sensor data into calibrated data using said conversion function.
 39. A computer system for calibrating a substantially continuous analyte sensor, the computer system comprising: a sensor data receiving module that receives a data stream comprising a plurality of time spaced sensor data points from a substantially continuous analyte sensor; a reference data receiving module that receives reference data from a reference analyte monitor, including two or more reference data points; a data matching module that forms one or more matched data pair by matching reference data to substantially time corresponding sensor data; a conversion function module that creates a conversion function using said one or more matched data pairs; a calibration evaluation module that evaluates one or more matched data pair, wherein evaluating said one or more matched data pairs includes at least one of 1) ensuring said one or more matched data pairs is within a predetermined time period, 2) ensuring said one or more matched data pairs is no older than a predetermined value, 3) ensuring said one or more matched data pairs have substantially distributed high and low matched data pairs over a predetermined time range, and 4) ensuring said one or more matched data pairs is within a predetermined range of analyte values, wherein said evaluation calibration module further evaluates at least one of a rate of change of the analyte concentration, a congruence of respective sensor and reference data in matched data pairs, and physiological changes, and wherein said conversion function module is programmed to re-create said conversion function if such modification is required by said calibration evaluation module.
 40. The computer system of claim 39, wherein said calibration evaluation module evaluates an initial one or more matched data pairs.
 41. The computer system of claim 39, wherein said calibration evaluation module evaluates one or more subsequently received matched data pairs.
 42. The computer system of claim 39, wherein said calibration evaluation module evaluates a plurality of matched data pairs.
 43. The computer system of claim 39, wherein said sensor data receiving module receives said data stream from an implantable analyte sensor.
 44. The computer system of claim 39, wherein said sensor data receiving module receives an algorithmically smoothed data stream.
 45. The computer system of claim 39, wherein said sensor data receiving module comprises programming to smooth said data stream.
 46. The computer system of claim 39, wherein said reference data receiving module downloads reference data via a cabled connection.
 47. The computer system of claim 39, wherein said reference data receiving module downloads reference data via a wireless connection.
 48. The computer system of claim 39, wherein said reference data receiving module receives within a receiver internal communication from a reference analyte monitor integral with said receiver.
 49. The computer system of claim 39, wherein said reference data receiving module receives reference data from a self monitoring of blood analyte.
 50. The computer system of claim 39, wherein said conversion function module comprises programming that performs linear regression.
 51. The computer system of claim 39, wherein said conversion function module comprises programming that performs non-linear regression.
 52. The computer system of claim 42, wherein said conversion function module creates the conversion function based on between one matched data pair and six matched data pairs.
 53. The computer system of claim 42, wherein said conversion function module creates the conversion function based on at least two matched data pairs.
 54. A computer system for calibrating a substantially continuous analyte sensor, the computer system comprising: a sensor data receiving module that receives a data stream comprising a plurality of time spaced sensor data points from a substantially continuous analyte sensor; a reference data receiving module that receives reference data from a reference analyte monitor, including two or more reference data points; a data matching module that forms one or more matched data pair by matching reference data to substantially time corresponding sensor data; a calibration set module that forms a calibration set comprising at least one matched data pair; a conversion function module that creates a conversion function using said calibration set; a sensor data transformation module that converts sensor data into calibrated data using said conversion function; and a calibration evaluation module that evaluates said calibration set when said new matched data pair is provided, wherein evaluating said calibration set includes at least one of 1) ensuring matched data pair in said calibration set span a predetermined time period, 2) ensuring matched data pair in said calibration set are no older than a predetermined value, 3) ensuring said calibration set has substantially distributed high and low matched data pair over a predetermined time range, and 4) allowing matched data pair only within a predetermined range of analyte values, wherein said conversion function module is programmed to re-create said conversion function of such modification is required by said calibration evaluation module, wherein said programming for determining a value for n determines n as a function of the frequency of the received reference data points and signal strength over time, and wherein the calibration set module further comprises programming for determining a value for n, wherein n is greater than one and represents the number of matched data pairs in the calibration set.
 55. The computer system of claim 39, wherein data matching module further comprises programming to form a set of matched data pairs responsive to said calibration evaluation.
 56. The computer system of claim 55, wherein said conversion function module further comprises programming to create a conversion function based on said set of matched data pairs.
 57. The computer system of claim 39 or 56, further comprising a sensor data transformation module comprising programming for converting sensor data into calibrated data using said conversion function.
 58. A method for calibrating a glucose sensor, the method comprising: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including one or more reference data points; providing at least one matched data pair by matching reference analyte data to substantially time corresponding sensor data; creating a conversion function based on at least one matched data pair; and evaluating at least one of said matched data pairs, wherein evaluating comprises at least one of evaluating a rate of change of the analyte concentration, evaluating a congruence of respective sensor and reference data in a matched data pair, and evaluating physiological changes.
 59. A computer system for calibrating a glucose sensor, the computer system comprising: a sensor data module that receives a data stream comprising a plurality of time spaced sensor data points from a substantially continuous analyte sensor; a reference input module that receives reference data from a reference analyte monitor, the reference data comprising one or more reference data points; a processor module that forms one or more matched data pairs by matching reference data to substantially time corresponding sensor data and subsequently forms a calibration set comprising said one or more matched data pairs; and a calibration evaluation module that evaluates one or more matched data pairs, wherein said evaluation calibration module evaluates at least one of a rate of change of the analyte concentration, a congruence of respective sensor and reference data in matched data pairs, and physiological changes.
 60. The method of claim 58, wherein the step of matching reference analyte data to substantially time corresponding sensor data comprises determining a best matched pair at least in part by comparing a reference data point against a plurality of individual sensor values over a predetermined time period.
 61. The method of claim 58, wherein the step of matching reference analyte data to substantially time corresponding sensor data is at least in part based on a time lag of at least about 5 minutes in the sensor data as compared to the reference data.
 62. The method of claim 58, wherein the step of matching reference analyte data to substantially time corresponding sensor data comprises matching a reference data point with an average of a plurality of sensor data points over a predetermined time period.
 63. The method of claim 58, wherein the step of evaluating at least one of said matched data pairs further comprises ensuring said at least one of said matched data pairs is within a predetermined range of analyte values.
 64. The method of claim 58, wherein the step of evaluating at least one of said matched data pairs further comprises evaluating a clinical acceptability of a disparity between the reference data point and time corresponding sensor data point of said at least one of said matched data pairs.
 65. The method of claim 58, wherein the step of evaluating at least one of said matched data pairs further comprises ensuring at least one of said matched data pairs is within a predetermined time range.
 66. The method of claim 58, wherein the step of evaluating at least one of said matched data pairs further comprises ensuring at least one of said matched data pair is no older than a predetermined value.
 67. The method of claim 58, wherein said at least one of said matched data pairs comprise two or more matched data pairs, and wherein the step of evaluating further comprises ensuring that the two or more matched pairs have substantially distributed values.
 68. The system of claim 59, wherein the processor module is configured match reference data to substantially time corresponding sensor data at least in part by evaluating a best matched pair by comparing a reference data point against a plurality of individual sensor values over a predetermined time period.
 69. The system of claim 59, wherein the processor module is configured match reference data to substantially time corresponding sensor data at least in part based on a time lag of at least about 5 minutes in the sensor data as compared to the reference data.
 70. The system of claim 59, wherein the processor module is configured match reference data to substantially time corresponding sensor data is configured to match a reference data point with an average of a plurality of sensor data points over a predetermined time period.
 71. The system of claim 59, wherein the calibration evaluation module is further configured to ensure one or more matched data pairs is within a predetermined range of analyte values.
 72. The system of claim 59, wherein the calibration evaluation module is further configured to evaluate a clinical acceptability of a disparity between the reference data point and time corresponding sensor data point of one or more matched data pairs.
 73. The system of claim 59, wherein the step of evaluating at least one of said matched data pairs further comprises ensuring said at least one of said matched data pairs is within a predetermined time range.
 74. The system of claim 59, wherein the calibration evaluation module is further configured to ensure one or more matched data pairs is no older than a predetermined value.
 75. The system of claim 59, wherein said one or more matched data pairs comprise two or more matched data pairs, and wherein the calibration evaluation module is further configured to ensure that the two or more matched data pairs have substantially distributed values. 